IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
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IntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
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Designed to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
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After a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
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Our innovative Book Series format brings you:
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Topic Focused Publications - Each topic showcases high impact subject areas
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Renowned Editorial Expertise - Series Editors, Topic Editors, and a team of international Board Members that permanently support each Book Series
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Fast Publishing - quick turnaround which is unique for book publishing
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The benefit of ISSN and ISBN for increased citation and indexing possibilities
\n
\n\n\n\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\n
IntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
We invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
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Note: Edited in October 2021
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1. Introduction
Muscular tissue has a good ability to regenerate that promotes the healing of lesions also extended, caused by strains, contusions and muscle lacerations. This characteristic is guaranteed by myogenic precursor cells (satellite cells) that proliferate and fill the structure of the extracellular matrix produced by fibroblasts becoming myoblasts.
Several factors make difficult the healing of the lesion: hematoma, granulation tissue, scar tissue and lesions of nerve bundles. In \'84 Garret et al. [1] have shown that the tissue recovery was obstructed by a large percentage of denervated myocytes distal to the lesion.
A well-executed suture could allow early rehabilitation with a lower risk of re-rupture and stitch pullout. The main problem is figuring out which is the best surgical technique, but especially when a surgical repair is necessary and if the benefits outweigh the disadvantages.
In the choice of surgical technique are many points still debated. The surgical indication is very limited and, in most of the muscle injury, conservative treatment is certainly indicated. Location and extent of the lesion are the criteria most examined in the literature.
Some of the few in vivo studies on animals have shown that a surgical suture of the wound can accelerate healing. The myorrhaphy prevents the formation of excess scar tissue, limits the hematoma formation, decreases the infiltration of mononuclear cells, increases the number of regenerating myofibers, decreases the inflammatory response favoring the healing.
For example, lesions of the pectoralis major muscle have wider surgical indication and conservative treatment is recommended only in cases of injury at the sternoclavicular origin, in some partial tears, in older or sedentary individuals.
Some authors tend to wide the indication for surgical treatment to lesions greater than 50% of the thickness of the muscle belly and the debate is still open because of the few tests to date available, but certainly the indication is strengthened by the absence of synergistic muscles that can decrease the workload to the muscle and therefore aid healing.
The aim of this chapter is to perform a review of the literature in order to identify the muscle injury which indicate surgical treatment and its results.
2. Muscle healing
Muscle injuries are debilitating injuries, especially for athletes who risk setbacks in their career. A subcutaneous muscle tear can be caused by direct trauma, such as a contusion from a blunt object or strains, or by indirect trauma such as ischemia or a neurological dysfunction [2]. In any case these injuries are difficult to treat and unfortunately there are no clear and defined guidelines to help the physician [3].
From a biological point of view muscles have been shown to be particularly active and capable of excellent tissue regeneration. The gap between the muscle fibers is filled thanks to the myocyte cell reactivity, the presence of replicative phase cells and the production of connective tissue scar.
The healing process of muscle injury consists of three phases: the degeneration-inflammation phase, the reparative phase and the remodeling-fibrosis phase (Figure 1).
The first phase (first few days post-injury) is characterized by inflammatory stimulation caused by the cellular debris and the pro-inflammatory molecules that are released as a result of necrosis of the injured tissue; also the severed blood vessels release blood within the tissue forming hematoma, which in turn stimulates the inflammatory response. This cascade of events results in the release of cytokines, interleukins, adhesion molecules (e.g., P-selectin, L-selectin, E-selectin), Tumor Necrosis Factor alpha and growth factors (e.g. insulin-like growth factor 1 IGF-1, hepatocyte growth factor HGF, EGF, epidermal growth factor, transforming growth factor alpha and TGF beta, platelet-derived growth factor PDGF) that promote inflammation, cell migration and stimulate progress to the next stage [2 - 3]
The reparation phase (from day 7-10 to week three-four post-injury) begins with the cleaning of the tissue formed in the acute phase by macrophages that engulf the injured tissue and allow the regeneration of tissue within the lesion: it stimulates the proliferation of striated muscle tissue, the neo-angiogenesis within the neo-tissue and stimulates the production of connective scar tissue. The cells that are more active from the point of view of replication (myogenic precursor cells, or satellite cells) are located between the basal lamina and the plasma membranes of each individual myofiber; once they are released by the lesion of the basal lamina and activated by growth factors, they differentiate into myoblast and replicate forming multinucleated myotubes and possibly myofibers.
The final phase, the remodeling-fibrosis, involves the maturation of the neo-muscle tissue and the reorganization of the scar tissue, and is strongly driven by mechanical stress and the stress of the surrounding tissue that drive the neo-tissue to organize in the most functional way possible for contraction. The connective tissue produced is partly demolished, gradually leaving more space for the connections between the myofibers [4].
Figure 1.
The diagram summarizes the three phases of muscle laceration healing. Note that reparation has a peak around the second week and concludes by the third- fourth week [2].
Fibroblasts that colonize the lesion and produce extracellular matrix play a key role in lesion healing. The connective tissue that is formed fills the gap created by the lesion with a three-dimensional plot that not only guides the proliferation of muscle cells and blood, but allows the transmission and the distribution of mechanical stresses thus acting as a sort of brace and allowing the functional use of the muscle before the lesion is completely healed. It was observed that animal muscles, thanks to this connective tissue, already after 14 days the scar that had formed in complete tears was mechanically more resistant than the surrounding muscle [4]. It should be kept in mind that in human tissue healing times are longer and recovery is less complete but the sequence and function of the repair steps are the same [5].
This scar tissue is extremely important but it can also be an obstacle to proper healing: in fact, if there is too much of it, instead of promoting, it may prevent tissue proliferation leading to incomplete recovery [3]. A proper healing of muscle tissue is centered on the correct balance between fibroblastic proliferation and myoblast proliferation: the first promote the connective tissue that must act as a scaffold for the repopulation of the lesion by the myoblasts. When the lesion is too large, however, the gap between the proximal and the distal stump is filled with granulation tissue which results in connective tissue scar [6, 7] leaving little room for myoblast proliferation. It is possible that in the final phase (remodeling) structural improvements of the scar may occur but they are only minor [5].
The healing of the lesion depends not only on cell reactivity and the amount of scar tissue, but is closely linked to many other factors. The innervation of the tissue remaining promotes tissue viability: an excessive presence of denervated myocytes downstream of the lesion impairs proper healing [1]. Other aspects to be considered are the supply of oxygen from the surrounding tissue, the vascular proliferation and neo-angiogenesis within the lesion in the post-trauma stage, the percentage and the pattern with which the myoblasts go to form the myotubes and the collagen crosslinking [8].
3. Indication for surgey
Unfortunately very few studies are available in literature that scientifically demonstrate the benefits of one treatment rather than another.
Traditionally muscle injuries are treated conservatively and surgery is frowned upon by many surgeons for this type of lesions of the musculoskeletal system. It is common belief that surgical treatment gives results similar to, or even worse than conservative treatment [9]. Therefore it is not recommended for the fear of causing damage which would lead to further complications. The presence of hematoma and a palpable gap in the muscle belly make surgical suture difficult to perform because it is often impossible to get the fascial ends to close and the muscle fibers are hard to draw back together [5].
However, animal studies have shown that surgically sutured muscle heals more quickly and more functionally. The suture in fact decreases the distance between the muscule stumps allowing a more rapid recovery [10], decreasing major defects in scarring [11], improved healing and a decrease in deep superabundant scar tissue [3].
Obviously, surgery can lead to numerous, although fortunately infrequent, complications; therefore it is a viable option only when it guarantees clear and obvious improvements for the patient or when the lesion cannot heal if treated with the conservative approach.
Generally, surgical treatment is indicated for severe muscle tears [1, 12], in grade 4 lesions on the Ryan classification [13, 14] or when over 50% of the muscle fibers are involved. Grade 1 refers to injuries of a few muscle fibers; grade 2 - injury of a moderate number of muscle fibers; grade 3 - rupture of a moderate number of fibers associated with partial lesion of the fascia; grade 4 -those that involve injury through the full thickness muscle and the fascia (Figure 2).
Figure 2.
Ryan\'s Classification for muscle damage. The thin lines represent the muscle fibers, the thick lines represent the fascia.
From the clinical point of view, a massive rupture of the muscle leads to a loss of its strength which may be acceptable in patients with a low functional demand. However, athletes or people with high functional demands need the full recovery of muscle strength which conservative treatment in some areas and in some types of injury cannot guarantee. In literature, little has been written on myorrhaphy of the skeletal muscle and almost all of the reports focus on injuries of the biceps brachii and petctoralis majors. If these two muscles are severely damaged, a major deficit in strength and the cosmetic damage is not to be underestimated in the case of professional body-builders. The suture of muscle damage is in fact taken into consideration if the action of the muscle is not compensated for by other synergistic muscles or if the hematoma is so large that it compresses the main vessels and determines ischemia of the tissue or overlying skin [15].
The timing of the intervention is not clearly defined in the literature, Kragh et al. [16] recommend to do it the day after the traumatic event because delay it causes the suffering of muscle tissue and of the epimysium.
4. Pectoralis major
A complete lesion of the pectoralis major still allows a normal active mobilization of the shoulder, but will cause an important decrease in strength in adduction and internal rotation of the arm [17] so an early surgical reconstruction is indicated in athletes. In fact, with conservative treatment a significant loss of torque, measured using the isokinetic strength test, has been observed [18, 19] while following surgical treatment a significant increase in isokinetic torque of the muscle has been reported [20, 21]. In addition, the aesthetic damage caused by the gap following injury can cause the end of the career for body-builders and similar professionals for whom the aesthetics of the muscle is essential. Conservative treatment is recommended only in cases of injury at the sternoclavicular origin, in some partial tears, in older or sedentary individuals.
This lesion was classified and reported in literature for the first time by Patissier in 1822 and since then over 200 cases have been reported [22]. The number of these lesions has increased and the increase in the last 30 years is due to the increased participation in heavy physical activity such as weight lifting, weight training, wrestling, rugby and waterskiing. Attributable almost solely to males, these lesions occur in an age range from 16 to 91 years with a peak in athletes aged between 20 and 40 years [22]. The most common mechanism is indirect trauma during bench pressing or other weight lifting movements; less frequent injuries are those following abduction-external rotation, extension-adduction and direct trauma [23].
The lesion of this muscle may occur in different zones. Tietjen [24] used a classification divided into 3 groups: type I consists of muscle contusions and sprains; type II partial tears and type III complete lesions. Each of these groups has a subclassification based on the location of the lesion: A - muscle origin; B - belly muscle; C - myotendinous junction; D - tendon. Statistically, insertion tendon injuries are the most frequent (55%), lesions to the belly or muscular-tendon insertion cover about 35% of these injuries [23].
According to the most recent meta-analysis [23] early surgical treatment achieves significantly better results than conservative treatment with an excellent-good result in 90% of cases, compared with 17% of conservative treatment (P = 0.00000001). When surgery is delayed slightly worse results are obtained, especially after 13 weeks from injury.
While for partial lesions treatment with conservative immobilization for 2-3 weeks is indicated, where surgery is required immobilization should be 4-6 weeks and subsequently a gradual lengthening of the muscle must be performed and only after 6-12 weeks strength training can begin.
5. Biceps brachii
The lesion of the biceps usually involves the tendon portion, more often the long proximal head and to a lesser extent the distal end that inserts into the radial tuberosity. Sometimes biceps muscle tears occur in the belly, especially when the arm remains entangled in ropes during some sports such as skydiving and wakeboarding [25, 26]. Less frequently, the muscle belly can break following direct trauma in a car accident [27].
Complete laceration of the muscle belly of the biceps brachii leads to a loss of muscle strength and unfortunately, few data are available in literature regarding the right choice of treatment. One of the few studies to have compared the effects of a surgical muscle suture with conservative treatment is the one by Kragh and Basamania in 2002 [25]. In a military base in North Carolina with 25,000 paratroopers, the authors performed nine surgeries in a year and compared the results to those of three patients treated conservatively in previous years. The paratroopers treated surgically achieved significantly better results for strength (measured in supination torque) and appearance. Those who had been treated conservatively had a worse appearance, suffered easy fatigue in repetitive movements of pronation and supination (e.g. use of screwdriver), avoided weight-lifting and in two cases the musculocutaneous nerve was in contact with the skin and not covered by muscle tissue thus easily irritated even by the cuffs on the uniform.
The mechanism of injury is important for the occurrence of lacerations. In parachutists, the entrapment of the arm in the static-line during the launch determines a subcutaneous transverse lesion and an almost total one of the muscle belly with the proximal and distal stumps retracted; in this case muscle suture is indicated. In the case of water sports such as water skiing or wakeboarders, the mechanism of injury is similar with the entrapment of the arm in a tow-line that pulls the athlete at great speed. What differs between the two sports is that the lesion usually reported in water sports causes a tearing injury in the proximal-distal direction of the biceps and is not as clear cut as the paratroopers; this sometimes may preclude the muscle suture and require muscle resection [26].
The injury of the muscle belly of the biceps brachii is rare and closely related to particular sports; the indication for surgery is clear as it prevents functional and aesthetic deficits.
Kragh et al. in [25] with regard to the lesions of the biceps brachii recommend the intervention in the case in which the lesion extend more than 95% of the muscle belly.
6. Quadriceps femoris
No cases of suturing subcutaneous belly lacerations of the quadriceps femoris are described in literature. The only description is in a surgical case report by Straw [28]. The tear had occurred at the proximal musculotendinous junction. With surgery muscular performance improved 151% to concentric power after 6 months compared to preoperative levels.
7. Hamstring muscle
The hamstring is one of the muscle groups most affected by injuries in athletes. They often suffer strains localized in the muscle-tendon junction due to an eccentric contraction. The lesions may occur in different areas: proximal or distal muscle-tendon junction, muscle belly, the proximal and distal tendon insertion [29].
Usually hamstring injurues are treated conservatively with rest, ice, physical therapy, NSAIDs and a gradual return to sport. Rarely a lesion in this anatomical site requires surgical treatment.
One of the rare occasions when the patient may have to undergo surgery is when the hamstring is detached from the ischial tuberosity. This injury is rare but its incidence is increasing, especially in middle-aged patients who continue to be physically active [30]. The triggering mechanism of injury is a sudden flexion of the hip and extension of the knee that causes a contraction of the hamstring. The patient reports feeling a shot in the rear thigh and walks with his leg straight (stiff-legged gait) avoiding flexing hip and knee in order to relieve the pain.
The hamstring avulsion from the ischial tuberosity is not always treated surgically. Presently there is no commonly accepted classification, or any guidelines to be followed for treatment [29]. The current literature recommends conservative treatment if the injury affects only one tendon and the retraction does not exceed 1 - 2 cm. The single damaged tendon tends to heal by adhering to intact neighboring tendons and even professional athletes will be able to return to competitive activity within about 6 weeks from injury.
The rupture of three tendons usually causes a major retraction of the stumps (greater than 5 cm) and in this case surgical treatment is recommended.
When two tendons are completely damaged, the regimen is not clear and depends on the physician\'s personal experience. Surgery is generally recommended if the patient is less than 50 years old, active and participating in athletic activities. Furthermore, the retraction of the stumps for more than 2 cm may be the indirect sign of injury to the third muscle at the muscle-tendon junction or in the muscle belly; that has gone unseen at diagnosis, making surgery absolutely necessary [30]. It should be remembered that the MRI is difficult to interpret with certainty when multiple tendons are damaged in this region.
Conservative treatment lead to a reduction of strength in the knee flexion and extension of the hip; also a superabundant scar could entrap the sciatic nerve, which runs nearby, giving rise to a "hamstring syndrome". The cause of neurological symptoms in the sciatic nerve is not well defined and remains ambiguous: it is possible that the fibrotic scar tissue generated from the lesion could lead to adhesions with the nerve, it is possible that the detached bone of the tuberosity forms a callus which compresses the nerve, or that the hamstrings during retraction cause the sciatic nerve or one of its branches to stretch [31].
Surgical treatment provides for an incision (longitudinal or transverse) on the posterior side of the proximal thigh, once tendons are isolated, the surface of the ischial tuberosity is debrided, the injured tendons are reinserted with bone anchors and if necessary, the neurolysis of the sciatic nerve is performed to free it from any adhesions.
There is no "one and only" postoperative care in literature; some authors recommend to avoiding using a cast; at least one suggests keeping the knee flexed at 30 ° [29], others recommend a splint at 90 ° for 2 weeks followed by a gradual lengthening of the knee [31], others advise protecting the affected hip with a cast to be worn for 6 weeks that allows only movements between 15 ° and 30 ° of flexion [30]. This is followed by a a gradual recovery of the joint\'s ROM and muscle strength during which time it is important to avoid too much tension on the insertion of the proximal hamstring in the first post-operative period. In particular, passive aggressive stretching should be avoided in the first 3 months post-op [29]. The return to sports-specific conditioning is expected between 6-9 months after surgery. It should be kept in mind that the full recovery of muscle strength requires a long time; often more than a year is needed to obtain an equal force in both legs. Residual muscle atrophy is common, especially in the long head of the biceps which results in a compensatory hypertrophy of the short head [32].
In the preoperative period hamstring stretching exercises should be avoided to prevent further distancing of the ends of the lesion; instead isometric contractions of the quadriceps and gluteal muscles should be encouraged to reduce muscle atrophy as well as ankle pumps to prevent deep vein thrombosis [30].
The results of surgical treatment reported in literature appear to be satisfactory with 62 -90% of patients satisfied with return to sporting activity levels the same as before the injury [33, 34, 35, 36]. A recent review of the literature [37] shows that early surgical treatment guarantees a better performance in terms of rate of return to sports, patient subjective satisfaction, recovery of strength and performance compared to conservative treatment. Unfortunately the results presented in literature are not easy to interpret because the techniques used are varied and, being a rather rare lesion, the cases analyzed are few.
The results of the operation depend on many factors. The timing of surgery seems crucial as after 14 days from injury there is a higher risk of adhesions between the lesion and the sciatic nerve [38] so surgery should be performed before this limit. If surgery is performed after five weeks, the post-operative cast must be worn for a longer perid of time [39]. It is not clear how the retraction of the tendon influences the treatment decisions : while in children retraction is an important factor in avulsion fractures, in skeletally mature subjects the importance of retraction has not yet been demonstrated. It has been shown how the level of retraction changes if observed on MRI or intra-operatively and it is closely related to hip and knee flexion and muscle activation [29].
Another rare lesion that can take place to the hamstring is a lesion to the distal insertion. In literature this injury is briefly described and the need for surgery is indicated although no more than 10 case reports and one case series with only a few patients enrolled, have been published.
The most common in the distal region is the avulsion of the biceps femoris associated with a multiligamentous lesion of the knee with a trauma in varus and hyperextension [40]. This muscle is an important and strong flexor and an active stabilizer of the knee and its injury can cause weakness and instability of the knee, especially in athletes [41]. Moreover, in the case of partial tears, the haphazard rearrangement of scar tissue and the formation of adhesions may jeopardize competitive sport activity because it causes pain. From an analysis of the 19 cases present in literature [41], it can be concluded that this type of injury, both complete and incomplete, allows the return to competition and that sometimes conservative treatment of partial tears may eventually require surgery to stop the painful symptoms.
The semimembranosus muscle has a role in flexion and internal rotation of the tibia. An injury to its distal portion determines a worse prognosis for players than the biceps femoris. There are fewer reports in literature (6 cases) [41], and it seems that the early reconstruction of total lesions lead to good results (2 cases). However, in the case of partial lesions, despite late reconstruction (4 cases), the subjects were unable to return to the same level of sporting activity as performed before previous to the injury. Biopsies performed on the unhealed muscles showed a severe denervation of muscle fibers. We can therefore suppose that a partial tear of the distal myotendinous junction of this muscle can cause serious nerve damage and determine a worse prognosis [41].
The semitendinosus muscle contributes to flexion strength at high degrees of knee flexion and intrarotation of the tibia. The tendon of this muscle is often harvested for use as a graft for reconstruction of the anterior cruciate ligament, so in theory it is an "expendable" tendon. Indeed it has been shown that athletes who have had removed their semitendinosus tendon were able to return to the previous level of activity without the harvest causing an important deficit in the competitive performance [42]. A traumatic injury of this region, however, can result in a partial or total lesion that causes pain, and interferes with the physical activity of the patient. Also in this case few articles are available in literature and it is not clear when it is advisable to choose conservative treatment and when to opt for surgical treatment. From our literature search we found 25 cases described [40, 41, 43, 44], 14 patients had been treated conservatively and 11 surgically. The surgery in all cases was a tenotomy with eventual release of adhesions, in no case was a suture performed to restore the damaged anatomical structure. In 5 of the 14 cases (35%) treated conservatively there was a treatment failure and the patient needed surgery that subsequently gave good results. Of the 11 patients treated surgically al returned to their previous level of athletic activities.
The study with the largest case series [40] emphasized that early surgical treatment (within 4 weeks from injury) leads to a much earlier return to sports: with conservative treatment (7 cases) return to sport occurs at an average of 18.4 weeks, with surgery (5 cases) at 6.8 weeks, while if conservative treatment fails and surgery is needed (5 cases), return to the field is much later, at 29.6 weeks on average. Unfortunately, however, the results are not statistically significant because of the small sample number.
8. Adductor muscle
A lesion at the proximal end of the abductor complex is not a common injury. There are few articles in literature about it, and those few are of a low level of evidence and with a few selected cases. An eccentric contraction during a movement of forced abduction of the lower limb may cause this type of acute injury. The most common mechanism is bilateral abduction of the lower limbs with a hip flexion and internal rotation of the other hip [45]. Usually the acute injury is preceded by painful symptoms previous to the accident that express an acute injury on a chronic disease in the area. In fact, histological studies [46, 47] have shown that in the area near the lesion there are degenerative tissue alterations. For this reason the first therapeutic approach to acute injuries of proximal abductor insertion was based on the results of treatment of the chronic lesion. Tenotomy, often used for chronic pain resistant to conservative therapies, had not guaranteed excellent results: 40% of patients were unable to return to competitive activity after surgery and the strength of the adductors reported a significant decrease at the isokinetic test [48]. These results led to the surgical reconstruction of the injured tendon / muscle to prevent the loss of strength and try to increase the percentage of athletes who could return to their previous level of competitive activity [46].
It was later shown that about 60% of the athletes complained of an aspecific symptom, especially abdominal or inguinal pain in the period prior to the injury [49]. However, the importance of the integrity of these anatomical structures has decreased. Studies on the conservative treatment of these lesions have shown that the anatomical continuity of these structures is not essential for a high athletic performance and electromyographical studies support the idea that the abductor muscles do not play a key role in sprinting and cutting movements [50, 51].
The most recent study with the largest series includes 19 players of the American National Football League (NFL) [49], 14 were treated conservatively and 5 surgically. The authors conclude that although all the players returned to play in the top league, those who received conservative treatment returned to the field sooner (6.1 + / - 3.1 weeks) than those treated surgically (12.0 + / - 2.5 weeks). Moreover, besides the risk of complications with surgery, the operation is not easy to perform if the lesion is at the level of the muscle-tendon junction [45].
The information available in the literature on this subject is scarce, with a low level of evidence and often conflicting. Some authors recommend surgery to suture / reinsert in the case of acute injuries occurring in athletes and excision / tenotomy in the case of inveterate injuries; other authors do not recommend surgery because it extends recovery time and does not guarantee better results than conservative treatment, considering also the non fundamental function of this muscle group in sport activities. Much still needs to be understood in this regards and future studies should be conducted with better methods and possibly with a larger number of patients.
9. Suture versus immobilization
Few studies in the literature have compared muscle suture and immobilization. Animal studies, usually in the Sprague-Dawley rat, have shown that lesions performed for experimental purposes heal better if sutured. Almekinders [52] has shown that the benefits obtained by suturing muscles are significant at one week after injury, while at two weeks surgical treatment or treatment with simple immobilization bring the same results in terms of maximum failure load, active force generation as well as from the histological point of view. Menetrey [3] instead found significant improvements with suturing muscle in respect to not suturing and immobilization; in fact one month after the injury the sutured muscle produced 81% of the tetanus strength misured in the intact muscle, while the non sutured muscle produced 35% and the immobilized muscle 18%. It is evident how the overabundance of connective tissue in the scar tissue inhibits the formation of myofibers at 12 weeks [1] whereas the suture prevents the formation of scar tissue in depth; it restricts the formation of hematoma by decreasing the gap of the lesion and the infiltration of mononuclear cells is limited to the surface region only. Desmin\'s detection has proved that the greatest number of regenerating myofibers is in the sutured muscle already at 2 days after injury, this is not because it limits the inflammatory phase or cellular necrosis (that occurs anyway) but probably because it produces a microenvironment favorable to repair, keeping the muscle stumps together [3].
Clearly the results of the animal tests should be interpreted with caution for several reasons: the lesion that is created is a surgical one, metabolism and healing of the lesion are different between humans and rats. In humans, the only studies comparing surgery and conservative treatment are about lesions of the biceps brachii [25, 53] where the best results are had in patients treated with the myorrhaphy. However, these articles have a poor methodology and take into account only a small number of patients; obviously future larger and better conducted studies are required in order to determine more specifically which type of treatment is most suitable in humans. It remains clear, however, that surgical treatment is rarely necessary, only for certain types of injuries, and only with a specific indication. We perform surgery only when strictly necessary.
10. Types of sutures
Once you choose a surgical approach it is important to decide the surgical technique and how to suture the muscle.
The suture of a muscle belly is easier in its proximal third and distal third, whereas only in some muscles in the middle third; this is because the tendon fibers flatten and extend into the muscle belly giving the stitches a greater support [54].
Literature describes many types of knots and sutures, such as the conventional Kessler, the modified Kessler and the figure-eight horiziontal mattress and more complex ones, such as the Mason-Allen, the Modified Mason-Allen and suturing the perimeter of the lesion.
The integrity and viability of the remaining muscle tissue is important and indicative for a good prognosis; furthermore the preservation of the epimiysium and the possibility of suturing it to the muscle makes the suture more resistant. From an in vitro study on pig muscles [55], it has been shown that suturing the epimysium gives the suture greater resistance to tensile stress compared to sutures made only on he muscle tissue and perimysium. This is because the epimysium consists of more connective tissue and is composed of two layers, hence much more resistant to tension compared to the perimysium [56].
The Kessler stitch would seem to be more resistant to pull-out suturing than simple stitch or simple suturing with a tendon graft [57].
Kragh [16] compared the Kessler stitch and a stitch combinations (Mason-Allen stitch and stitch around perimeter) in pig muscle. These two types of sutures were considered the strongest in a pilot study carried out before the main study where 9 different types of suturing were compared: simple stitch, running simple (epimysium based, non-core) stitch, the figure of eight stitch, the modifed Kessler stitch, a vertical mattress stitch, a horizontal mattres stitch (core), a horizontal mattress stitch (inverted, epimysium based, non-core), a double right angle stitch, a combination (Modified Mason-Allen and perimeter) stitch. At the tensile tests carried out, the Kessler stitch achieved a maximum load of 35 N, whereas the combined suture achieved 74 N. Not only did the combined suture achieve a greater tensile load, but also the Kessler stitch failed because the sutures were pulled away from the muscle tissue, whereas in the combined suturing, the better distribution of the forces induced a gradual lengthening of the muscle fibers and the stitches were not torn away from the tissue at the 35 mm lengthening.
Similar results were obtained on fresh frozen cadaveric human tissue [58]. Comparing Kessler, figure eight, mattress, Mason-Allen, perimeter and perimeter-Mason-Allen on different muscles of human cadaver, Kessler stitch was the least resistant and tore the muscle with an average load of 1.65 kg; the strongest suture, as was observed in other studies, was the combined suture, Mason-Allen plus perimeter stitch, that withstood a weight of 6.4 kg on average. It was also observed that the simple sutures tend to tear the tissue and the epimysium longitudinally, whereas the more complex sutures failure involves more the transversal tissue. In fact, the epimysium is the key to a robust suture, it is more robust in the tissue where the suture can adhere firmly, the complex sutures involve a greater surface area than the simple ones.
It should also be noted that the simple sutures close the epimysium flaps but in the deep layers, fibers are free and when subsequent contraction of the muscle flaps occur, although held together at the extremity by the suture, deep below the surface tend to form a gap that favors the formation of hematomas, prolong the period of healing and promote excessive fibrosis which may in turn lead to exuberant scar tissue. Hence, the complex suture allows the surgeon to pull together the edges of the tear both at the extremes and deep in the muscle enhancing a greater biological performance [58].
The muscle tissue, due to its physical characteristics, does not offer a solid anchor for sutures which, if positioned improperly, tend to tear the fibers and are pulled from the muscle. When the suturing encompasses multiple points on the injured muscle and the correct technique is used, it can sustain heavy loads and prevent further injuries and ineffective sutures. In theory a stronger suture and one that is less damaging to the muscle tissue should allow earlier mobilization without the risk of failure; therefore improving the healing, shortening the period of immobility and in turn decreasing muscle atrophy.
These in vitro tests demonstrate how to make the most of myorrhaphy. In vivo, however, there are no significant differences between the stitches used Even when comparing a Kessler stitch with a simple suture in a tendon graft, Chien et al. [57] found no difference in terms of muscle healing in rabbits.
Some authors have proposed the use of grafts to reinforce the suture, but It is still unclear whether the use of an augmentation graft suture, as performed by Botte et al. [59] on a case series of 58 patients, is useful to make improvements in clinical and functional outcomes.
11. Conclusions
The indication for surgical treatment applies to a small number of muscle lacerations. The tearing of the muscle belly is a common occurrence in athletes and nowadays are difficult to predict or prevent. There is no clear indication for surgical suture for these lesions and there is no real guide line to follow. The majority of the authors in literature consider performing surgery when the lesion affects more than 50% of the total of muscle fibers. Such an extensive injury would provoke a massive scar reaction making it difficult to achieve efficient and functional tissue leading to an excess of collagen and fibrotic tissue which would change the muscle mechanics and facilitate the onset of new lesions. It has also been evaluated and demonstrated that large lesions also present decreased strength, especially in those cases where the affected muscle is not assisted by other agonist muscles. The best results were obtained in patients treated with surgery compared to those treated conservatively with splinting.
In addition, such an extensive lesion would also be very disfiguring in appearance for those who make fitness and muscle shape their job such as body builders. These lacerations leave the muscle anatomy altered, and conservative treatment fails to restore the geometric lines and shapes of the muscle.
The muscles most affected by these injuries in sports are the pectoralis major (in lifters), the biceps brachii (in the paratroopers and water sportsmen who are pulled by ropes) and the rectus femoris.
Surgery brings the margins of the lesion together thus decreasing the hematoma and reducing the reactive fibrous reaction which in turn leads to smaller scars. The approach also enhances a faster return to mobilization of the affected segment and an earlier recovery of muscle tone.
The muscle belly tissue is not a robust structure for anchoring the stitches to, therefore the choice of method, anchor points and suture type must be made carefully.
It is evident how few publications there are in literature that deal with the management of massive muscle injuries; only a few studies comparing the therapeutic options have been conducted and the majority of articles available concern in vitro studies or animal studies. It is necessary that further studies are conducted in order to obtain enough scientific evidence to guide the treatment and management of these patients.
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Introduction",level:"1"},{id:"sec_2",title:"2. Muscle healing",level:"1"},{id:"sec_3",title:"3. Indication for surgey",level:"1"},{id:"sec_4",title:"4. Pectoralis major",level:"1"},{id:"sec_5",title:"5. Biceps brachii",level:"1"},{id:"sec_6",title:"6. Quadriceps femoris",level:"1"},{id:"sec_7",title:"7. Hamstring muscle",level:"1"},{id:"sec_8",title:"8. Adductor muscle",level:"1"},{id:"sec_9",title:"9. Suture versus immobilization",level:"1"},{id:"sec_10",title:"10. Types of sutures",level:"1"},{id:"sec_11",title:"11. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Garret WE Jr, Seaber AV, Boswick J, Recovery of a skeletal muscle after laceration and repair. J Hand Surg 1984;9A: 683-692'},{id:"B2",body:'Huard J, Li Y, Fu FH, Muscle injuries and repair: current trends in research. 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Surgical repair of acute traumatic closed transection of the biceps brachii. Journal of Bone and Joint Surgery. 2002;84°(6):992-998'},{id:"B26",body:'Pascual-Garrido C, Swanson BL, Bannar SM. Closed proximal muscle rupture of the biceps brachii in wakeborders. Knee Surgery Sports Traumatology Arthroscopy. 2012;20:1019-1021'},{id:"B27",body:'Shah AK, Pruzansky ME. Ruptured biceps brachii short head muscle belly: a case report. Journal of Shoulder and Elbow Surgery. 2004;13(5):362-365'},{id:"B28",body:'Straw R, Colclough K, Geutjens. Surgical repair of a chronic rupture of the rectus femoris muscle at the proximal musculotendinous junction in a soccer player. Br Journal of Sports Medicine. 2003;37:182-184'},{id:"B29",body:'Askling CM, Koulouris G, Saartok T, Werner S, Best TM. Total proximal hamstring ruptures: clinical and MRI aspects including guidelines for postoperative rehabilitation. Knee Surg Sports Traumatol Arthrosc. 21:515-533. 2013'},{id:"B30",body:'Cohen S, Bradley J. Acute proximal hamstring rupture. J Am Acad Orthop Surg. 15:350-355, 2007'},{id:"B31",body:'Chakravarthy J, Ramisetty N, Pimpalnerkar A, Mohtadi N. Surgical repair of complete proximal hamstring tendon ruptures in water skiers and bull riders: a report of four cases and review of the literature. Br J Sports Med. 39:569-572. 2005'},{id:"B32",body:'Silder A, Heiderscheit BC, Thelen DG, Enright T, Tuite MJ. MR observations of long term musculotendon remodeling following a hamstring strain injury. Skeletal Radiol. 37:1101-1109. 2008'},{id:"B33",body:'Klingele KE, Sallay PI. Surgical repair of complete proximal hamstring tendon rupture. Am J Sports Med. 30:742-747, 2002'},{id:"B34",body:'Chakravarthy J, Ramisetty N, Pimpalnerkar A, Mohtadi N. Surgical repair of complete proximal hamstring ruptures in water skiers and bull riders: a report of four cases and review of the literature. Br J Sports Med. 39:569-572. 2005'},{id:"B35",body:'Brucker PU, Imhoff AB. Functional assessment after acute and chronic complete ruptures of the proximal hamstring tendons. Knee Surg Sports Traumatol Arthrosc. 13:411-418. 2005'},{id:"B36",body:'Orava S, Kujala UM. Rupture of the ischial origin of the hamstring muscles. Am J Sports Med. 23:702-705. 1995'},{id:"B37",body:'Harris JD, Griesser MJ, Best TM, Ellis TJ. Treatment of proximal hamstring ruptures – a systematic review. Int J Sports Med. 32:490-495. 2011'},{id:"B38",body:'Konan S, Haddad F. Successful return to high level sports following early surgical repair of complete tears of the proximal hamstring tendons. Int Orthop. 34:119-123. 2010'},{id:"B39",body:'Chamichael J, Packham I, Trikha SP. Avulsion of the proximal hamstring origin: surgical technique. J Bone Joint Surg Am. 15:350-355. 2007'},{id:"B40",body:'Cooper DE, Conway JE. Distal semitendinosus ruptures in elite-level athletes: low success rates of nonoperative treatment. Am Journal Sports Med. 38(6):1174-8. 2010'},{id:"B41",body:'Lempainene L, Sarimo J, Kimmo M, Heikkila J, Orava S. Distal tears of the hamstrin muscles: review of the literature ans our results of surgical treatment. Br J Sports Med. 41:80-83. 2007'},{id:"B42",body:'Smith FW. Rousenlund EA, Aune AK. Subjective functional assessments and the return to competitive sport after anterior cruciate ligament reconstruction. Br J Sports Med. 38:279-284. 2004'},{id:"B43",body:'Schilders E, Bismil Q, Sidham S. Partial rupture of the distal semitendinosus tendon treated by tenotomy - a previously underscribed entity. Knee. 13:45-7. 2006'},{id:"B44",body:'Adejuwon A, McCourt P, Hamilton B, Haddad F. Distal semitendinosus tendon nrupture: is there any benefit of surgical intervention?. Clin J Sports Med. 19:502-504. 2009'},{id:"B45",body:'Campbell. Canale ST. Chirurgia ortopedica. X edizione. Verducci Editore. 2005'},{id:"B46",body:'Rizio L, Salvo JP, Schurhoff MR, Uribe JW. Adductor longus rupture in professional football players: acute repait with suture anchors - a report of two cases. Am J Sports Med. 32(1):243-245. 2004'},{id:"B47",body:'Ippolito E, Postacchini F. Rupture and disinsertion of the proximal attachment of the adductor longus tendon: case report with histochemical and ultrastructural study. Ital J Orthop Traumatol. 7:79-85. 1981'},{id:"B48",body:'Akermark C, Johansson C. Tenotomy of the adductor longus tendon in the treatment of chronic groin pain in athletes. Am J Sports Med. 20:640-643. 1992'},{id:"B49",body:'Schlegel TF, Bushnell BD, Godfrey J, Boublik M. Success of nonoperative management of adductor longus tendon ruptures in national football league athletes. Am J Sports Med. 37(7):13941399. 2009'},{id:"B50",body:'Mann RRMoran GT, Dougherty SE. Comparative electromyography of the lower extremity in jogging, running, and sprinting. Am J Sports Med. 14(6):501-510. 1986'},{id:"B51",body:'Neptune RR, Wright IA, Van Den Bogert AJ. Muscle coordination and function during cutting movements. Med Sci Sports Exerc. 31(2):294-302. 1999'},{id:"B52",body:'Almekinders LC. Results of surgical repair versus splinting of experimentally transected muscle. Journal of Orthopaedic Trauma. 1991;5(2):173-176'},{id:"B53",body:'Heckman JD, Levine MI. Traumatic closed transection of the biceps brachii in the military parachutist. Journal of Bone and Joint Surgery. 1978;60-A:369-72'},{id:"B54",body:'Chammout MO, Skinner HB. The clinical anatomy of commonly injured muscle bellies. J Trauma. 1986;26:549-552'},{id:"B55",body:'Kragh JF, Svoboda SJ, Wenke JC, Ward JA, Walters TJ. Epimysium and perimysium in suturing in skeletal muscle lacerations. The Journal of Trauma Injury, Infection, and Clinical Care. 2005;59(1):209-212'},{id:"B56",body:'Nishimura T, Hattori A, Takahashi K. Ultrastructure of the intramuscular connective tissue in bovine skeletal muscle: a demonstration using cell-maceration/scanning electron miscroscope method. Acta Anatomica. 1994;151:250-257'},{id:"B57",body:'Chien SH, Chen SK, Lin SY, Chen SS, Wu HS. Repair method and healing of skeletal muscle injury. The Kaohsiung Journal of Medical Schiences. 1991;7(9):481-488'},{id:"B58",body:'Chance JR, Kragh JF, Agrawal M, Basamania CJ. Pulout forces of sutures in muscle lacerations. Orthopedics. 2005;28(10):1187-90'},{id:"B59",body:'Botte MJ, Gelberman RH, Smith DG, Silver MA, Gellman H. Repair of severe muscle belly lacerations using a tendon graft. J Hand Surg Am. 12(3):406-412, 1987'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Giuliano Cerulli",address:null,affiliation:'
University of Perugia, Perugia, Italy
Nicola’s Foundation ONLUS Arezzo, Italy
Let People Move Research Institute Arezzo, Perugia, Italy
International Orthopedic and Traumatologic Institute Arezzo, Italy
“Santa Maria della Misericordia” Hospital, Orthopedic and traumatology department, Perugia, Italy
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1. Introduction
Increasing population elevated the demand for personal vehicles and hence evolved the advancements in the vehicular designs, engine designs, and integration of embedded electronics making the personal vehicles one of the most integrated technologies of the everyday life [1, 2]. With personal vehicles becoming ubiquitous in everyday life, there has been a rise in the associated risks. As per the data from the U.S. Census Bureau, 10.8 million vehicular accidents have been recorded in the year 2009 compared to 11.5 million accidents in the year 1990 [3] marking the reduction in accidents by 6%.
With the evolution of progressive intelligence systems popularly referred as Advanced Driver Assistance System (ADAS) comprising of lane departure warning systems, forward collision warning system, road signs (speed limit and speed regulatory) detection and recognition system, driver drowsiness and behavioral detection and alert systems, and also adoption of passive safety measures such as airbags, antilock brakes, tire pressure monitoring systems or deflation detection systems, automated parking systems, infrared night vision, pre-crash safety system and so on have not only increased the driver safety but also resulted in the reduction of associated risks as these technologies continuously monitor the driver as well as their and vehicular environment and provides timely information and warnings to the driver.
The detection and recognition of road signs is an important technology for the ADAS. Road signs are a guide to the drivers about the directions on the road, conditions of the road and serve as an essential warning under certain special road conditions. Thus, they enhance the road safety by providing the vital information. However, there might be the cases where a driver is distracted, is under stress of life, work or traffic, suffering lack of concentration or overwhelmed leading to overlook the road signs. Therefore, a system to monitor the road ahead of the vehicle, recognizing road signs and alerting about the vital conditions of the road would be an excellent assistance to the drivers. Pointedly, the road signs detection and recognition, which is the topic presented in this chapter cautions driver about the various road signs in a particular stretch of highways/roads enabling the drivers to drive within those limits, taking care of the road conditions and preventing from any over-speeding dangers.
The branch of computer science engineering that enables the machines which is the ADAS system in this case, to see, identify, interpret, and respond to the digital images and videos is termed as Computer Vision, abbreviated as CV. Until the boom of machine learning1 techniques, CV was largely depended on traditional digital image processing (DIP)2 methods, which are now mostly predicated on artificial neural networks (CNN)3. The impossible task for facilitating machines to respond to visions is achieved with the help of CV and it is intertwined with artificial intelligence4.
The field of CV comprises of all tasks similar to biological vision systems such as seeing, i.e., visual senses, perceiving what is seen, draw detailed information in a pattern in which it can be used for further processes ultimately providing appropriate responses. In short, it is a modus operandi to instill humankind tendencies to a computer. CV finds its applications in the field of multiple disciplines aiding in simulating and automating the functions biological vision system employing sensors, computers, and various embedded platforms in assistance with numerous algorithms.
The applications of CV are enormous and broader. Of those numerous applications, using CV for detection and recognition of road signs to aid the Advanced Driver Assistance Systems (ADAS) is pivotal. This chapter focuses on road signs, also termed as traffic signs, detection and recognition using the key CV techniques.
The novelty of this chapter includes: (i) the proposed CV based method detects and recognizes the speed limit and speed regulatory signs without any templates as the templates are part of the code and not the images. (ii) the proposed CSPJacinto-SSD network enhances detection accuracy while reducing the model parameters and complexity compared to the original Jacinto-SSD.
2. Computer vision in ADAS applications
Computer Vision (CV) is one of the crucial technologies in building the smart and advanced vehicles with autonomous driving capabilities termed as Advanced Driving Assisting System (ADAS). One of the key arena of active research of the ADAS is the road signs detection and recognition, which is a challenging task. A number of issues such as the type of camcorder, the speed of a car, noises in the image depending on speed and direction, type and intensity of light and weather conditions and sometimes the background and other objects that are similar to the signs makes it monotonous to detect and recognize the road signs. Additionally, road signs may also be damaged, faded out, tilted and partially submerged by other objects such as building signboards, trees, leading to confusion in the automated system. The process of detecting the road signs of all types is carried out using the images/videos candidates comprising of targeted road signs in case of both DIP methods and CNN methods. The road signs can be obtained from various datasets such as German Traffic Signs Dataset (GTSDB) [8], Tsinghua-Tencent 100 K [9], ImageNet dataset [10], Pascal VOC [11] to name a few. Most of these vastly used datasets may not have all the road signs in sufficient numbers captured under different lighting and weather conditions. This leads to researches to build their own datasets or rely on mechanical simulations such as CarSim [12] to build the lacking traffic signs.
This chapter discusses a low-complexity DIP algorithm and CNN based method along with the existing researches, product embodiments of these technologies followed by the algorithm design, hardware implementation and performance results of road signs detection and recognition.
2.1 Road signs detection and recognition
The process of locating the road signs from a moving vehicle followed by recognizing the exact type of road signs can be termed as ‘road signs detection and recognition.’ Although there are various approaches and different algorithms, some patterns may appear similar to that of an existing body of work as in Figure 1 that shows the basic steps employed in road signs detection and recognition flow. The process is generally divided into three parts namely, road signs detection to locate the potential candidates of road signs followed by the verification of the detected road signs’ candidates from the previous stages. Finally, the recognition of traffic signs to formulate the actual information from the detected and verified signs. This task of detecting followed by recognizing road signs to aid ADAS can be achieved through both DIP and CNN based methods.
Figure 1.
Basic steps of road signs detection and recognition.
Torresen et al. [13] presents a red-colored circular speed limit signs detection method to detect and recognize the speed limit signs of Norway. Moutarde et al. [14] presents a robust visual speed limit signs detection and recognition system for American and European speed limit signs. Keller et al. [15] presents a rectangular speed limit signs detection scheme aimed at detecting and recognizing the speed limit signs in United States of America (U.S.A.). A different approach is used by Liu et al. [16] wherein the de-noising method based on the histogram of oriented gradients (HOG) is applied to Fast Radial Symmetric Transform approach to detect the circular speed-limit signs. Zumra et al. [17] and Vavilin et al. [18] both uses color segmentation followed by other digital processing methods. Lipo et al. [19] presents the method that fusions camera and LIDAR data followed by the HOG and linear SVM to classify the traffic signs.
Sebastian et al. [20] presents the evaluation of the traffic signs detection in the real-world environments. The traffic signs are detected using the Viola-Jones detector based on the Haar features and Histogram of Orientated Gradients (HOG) relied on linear classifiers. Model-based Hough-like voting methods are tested on the standard-The GTSDB. It also discusses different methods proposed by the Ming et al. [21] that uses two different, supervised modules for detection and recognition, respectively. Markus et al. [22] uses modern variants of HOG features for detection and sparse representations for classification and Gangyi et al. [23] presents the method that uses the HOG and a coarse-to-fine sliding window scheme for the detection and recognition of traffic signs, respectively.
Supreeth et al. [24] presents color and shape based detection scheme aimed at detection of red color traffic signs that are recognized using the auto associative neural networks. Nadra Ben et al. [25] presents a traffic sign detection and recognition scheme aimed at recognition and tracking of the prohibitory signs. Then Feature vector extraction along with the Support vector mechanism (SVM) is used to recognize the traffic signs and the recognized traffic signs are tracked by the optical-flow based method of Lucas-Kanade tracker [26]. Y. Chang et al. [27] adopted the modified radial symmetric transform to detect the rectangular patterns and then Haar-like feature based AdaBoost detector to reject the false positives. Abdelhamid Mammeri et al. [28] proposed an algorithm for the North American Speed limit signs detection and recognition. There are plenty of state-of-the-art researches based on different models of CNN [29, 30, 31, 32] to detect and recognize the traffic signs including some hybrid approaches [33, 34].
2.1.1 Traditional digital image processing methods to detect and recognize road signs
The method employed to achieve certain operation on images with the aim of getting an enhanced image or extracting useful, interpretable information is termed as Image processing. It is similar to signal processing with the contraction that here input is an image and output is either an image or features affiliated with that image. In recent decades, image processing is among rapidly growing technologies. It forms the foundation for the computer vision and one of the core research area within engineering and computer science disciplines.
Fundamentally, image processing comprises of three steps namely, (i) Use of image acquisition tools to capture/import the images; (ii) Analyses and manipulation of the image; and, (iii) Output in which result can be altered image or report that is based on image analysis.
The methods used for image processing can be broadly classified in two, namely, analogue and digital image processing. Analogue image processing (AIP) refers to use of printouts and photographs to analyze via the basic interpretation employing visual techniques. On the other hand, digital image processing (DIP) techniques, as per the name, comprises of techniques that manipulate images digitally using computers. Pre-processing, enhancement, information extraction, and display are the basic, customary processes for all the data to undergo in DIP.
The process of detection and recognition of speed limit road signs [35] can be broadly classified into three stages namely, (i) speed limit signs detection, (ii) digit segmentation, (iii) digit recognition and that of detection and recognition of speed regulatory road signs [36] also into three stages such as, (i) speed regulatory signs detection, (ii) feature extraction, (iii) feature matching. Figure 2 depicts the proposed algorithm used in detection and recognition of the road signs. The following sections discuss each step of the algorithm and the corresponding implementation specifications of the respective stages.
Figure 2.
Flow chart of the proposed algorithm used to detect speed limit and speed regulatory signs.
A. Shape Detection
The process of detecting regular and irregular polygon shapes is termed as Shape detection. Shape detection in this chapter refers to detecting the road signs which processes the entire frame and then focuses on selecting the potential candidates of size 32x32, 64x64 and so on comprising the common shapes, either a circle or a rectangle of the speed limit sign or a triangular signs of the speed-regulated signs using radial symmetric transform method.
The concept of radial symmetric transform [37, 38] uses the axes of radial symmetry. The normal polygenes of n-sides possess several axes of symmetry and the radial symmetric transform works based on these symmetric axes.
The voting process is based on the gradient of each pixel [39]. The direction of gradient generates a vote. The vote generated from each pixel follows the symmetric axes resulting in the highest votes at the center of the respective symmetric axes. Figure 3 shows the radial symmetry for common polygenes.
Figure 3.
Radial symmetry of common polygenes.
Fundamentally, the Sobel operator [40] is applied to calculate the gradient of each pixel using a Sobel mask. Sobel operator generates the gradient of the intensities in the vector forms with the horizontal gradient denoted by Gx and vertical gradient denoted by Gy by convolving corresponding Sobel masks defining the direction of the gradients for each pixel. Besides, in order to eliminate the noises of small magnitudes, the threshold for the absolute values Gabs is set for horizontal and vertical gradient given by Eq. (1)
Gabs=∣Gx∣+∣Gy∣E1
Once the horizontal gradient Gx and the vertical gradient Gy is obtained and the noise is eliminated, the radial symmetric transform can be processed based on the calculated gradients. Figure 4 shows the results of the horizontal and vertical gradients.
Figure 4.
The results of the horizontal and vertical gradients.
Rectangular Radial Symmetric Transform
The voting process in the rectangular radial symmetric transform phase is based on the gradient generated from the Sobel operator [13, 27]. Each selected pixel with its absolute magnitude Gabs greater than a small threshold is denoted as p, and the gradient vector is denoted as g(p). The direction of g(p) can be formulated with the horizontal gradient Gx and the vertical gradient Gy into an angle using Eq. (2).
gp=tan−1GyGxE2
For each considered pixel p, the votes along with the known width W and height H are divided into two categories- horizontal vote and the vertical vote. The direction of gradient g(p) for each pixel is adopted to implement these two categories. The magnitude ranges and the ratio between Gx and Gy is used to verify the horizontal and the vertical votes with respect to the higher threshold and lower threshold values.
If Gx > higher threshold and Gy < lower threshold, vote is regarded as horizontal gradients.
If Gx < lower threshold and Gy > higher threshold, vote is regarded as vertical gradients.
Here, the values of higher threshold and lower threshold are experimentally chosen based on the size of the Sobel mask. In the case of 3x3 Sobel mask, the higher threshold is set in the range of 45–55, and the lower threshold is between 15 and 25. In case of the nighttime scenarios, both the thresholds are lowered to half of their original values and constrains are set for the ratio of horizontal and vertical gradients.
Each pixel contributes a positive vote and a negative vote. A voting line is then generated by each pixel with both positive and negative votes. The positive votes indicate the probable center of the speed limit sign while the negative votes indicate the non-existence of speed limit signs. The positive horizontal votes Vhorizontal+ and negative horizontal vote Vhorizontal- votes are formulated as in Eqs. (3) and (4). Lhorizontal (p, m) describes a line of pixels ahead and behind each pixel p at a distance W given by Eq. (5).
Vhorizontal+Lhorizontalpmmϵ−W2W2E3
Vhorrizontal−Lhorrizontalpm∣mϵ−W−W2∪W2WE4
Lhorrizontalpmroundm∗g¯p+W∗gpE5
where g¯(p) is a unit vector perpendicular to g(p). Figure 5(a) represents the process of horizontal voting. Similarly, the positive and negative vertical votes are formulated as in the Eqs. (6) and (7). Lvertical (p, m) describes a line of pixels ahead and behind each pixel p at a distance W given by Eq. (8), and as shown in Figure 5(b) where g¯(p) is a unit vector perpendicular to g(p).
Figure 5.
(a) The voting line corresponding to the horizontal voting. (b) the voting line corresponding to the vertical voting.
VverticalLverticalpmmϵ−H2H2E6
VverticalLverticalpmmϵ−H−H2∪H2HE7
Lverticalpm=p+roundm∗g¯p+W∗gpE8
After this voting process, the centers of the sign candidates will receive higher votes. The voting image is initially initialized to zero, and then it goes on accumulating both the positive and the negative votes. Figure 6 shows the result for rectangular signs after the voting process.
Figure 6.
The result after the horizontal voting process.
Circular Radial Symmetric Transform
The detection of circular speed limit signs using radial symmetric transform method is similar to that of the detection of the rectangular signs with a difference that the circular radial symmetric transform need not to be divided into two parts as horizontal votes and vertical votes. It is entirely based on the direction of gradient for each pixel g(p) and each considered pixel contributes only positive votes V+ as in Eq. (9).
V+=p+roundR∗gpE9
Figure 7(a) illustrates the voting process for the circular sign detection and Figure 7(b) shows the result for circular signs after the voting process.
Figure 7.
(a) The vote center corresponding to (9); (b) the result of the circular voting result.
Triangular Radial Symmetric Transform
The voting process of the triangular shape detection is also based on the gradient of each pixel [39]. The vote generated from each pixel follows the rule of the proposed triangle detection algorithm shown in Figure 8. It comprises of: (i) Sobel operator is applied to calculate gradient for each pixel. Consequently, we calculate the horizontal and vertical gradients by convolving the corresponding Sobel masks. Each selected pixel is represented with its absolute magnitude, and the gradient vector is denoted as g(p). The direction of g(p) can be formulated with the horizontal gradient Gx and the vertical gradient Gy into an angle as shown in Eq. (10). Only 180 degrees of gradient is used in this algorithm followed by the morphological erosion to eliminate noises. Morphological erosion is applied to eliminate the noise. For a pixel pxyfxyand a structure elementbij, the formula of erosion is as in Eq. (11).
Figure 8.
(a) Illustration of triangle detection algorithm; (b) illustration of voting for the center.
gp=tan−1GyGxE10
f⊖bxy=exy=minijfx+iy+j−bijE11
Then the proposed algorithm exploits the nature of triangle for the detection as in Figure 9(a). We look for the points having a gradient of 30 degree, defined as point A. Once the point A is obtained, we search for the points that have a gradient of 150 degrees on the same row as that of point A, defined as point B. The last step is to find the points C and D with 90 degrees gradient on the same column with points A and B, respectively. Once all these points are determined, a vote is placed to the point G at the centroid of the triangle as in Figure 9(b).
Figure 9.
The steps in detail of the sign candidate extraction (a) initialization of elements in the buffer to zero. (b), (c) insert the sign candidates based on the vote value. (d) if the current sign candidates own the greater vote value than any element in the buffer, firstly shift the element and the other elements in the wake of the right for the one-element and abandon the last element, and update the value to the element. (e), (f) post several iterations, the buffer is full of the sign candidates, and merge the cluster, only leaving the element with the greatest vote value. (g) the elements in red are merged.
In order to vote for the center point, the width of the detected position and the size of the target triangle is required to be calculated. The formulas shown in Eqs. (12) and (13), where Centerx is the x-coordinate of G, Centery is the y-coordinate of G, H is the height of the small triangle, and D is the size of the target triangle, as shown in Figure 9(b).
Centerx=Ax+Bx2;H=Bx−Ax∗32E12
Centery=Ay+D∗33−H,H<D∗33Ay+H−D∗33,H>D∗33E13
Then the width of the detected position is needed for calculating the detected points A and B. Thus, it is easy to build a look up table to reduce the computation cost of the voting process. The pixels with higher votes are judged to be in and near the center of the triangular road sign candidates. In order to reduce the computation cost, candidates that are close to each other will be merged into one candidate. The new coordinates of the candidate is the weighted arithmetic mean calculated using the coordinates of the merged candidates weighted by their vote thereby reducing the different candidates representing the same triangle.
B. Sign Candidates Extraction
After detecting the shape, the potential candidates of the road signs are extracted. A buffer is created to save all the potential sign candidates according to the following steps:
Initially, all the vote values in the buffer are set to zero.
For each vote in the input image, if the vote is greater than an experimentally set threshold, the pixel is considered. The considered vote is arranged into the buffer based on its vote value, ensuring the buffer is in a decreasing order. Every time this buffer is sorted in a decreasing order to ensure that, the pixels with greater values are in a prior order.
The values thus generated by the votes of the sign candidates result in a cluster of candidates in a small region. To overcome this challenge, the distance and search is defined from the prior order in the buffer by setting a small distance threshold to merge the cluster of sign candidates using non-maximum suppression as per Eq. (14). where x and y are the coordinates of the current considered sign candidates, and xi and yi are the sign candidates of the threshold candidates. Figure 9 illustrates the details of the sign candidates’ extraction along with the results of merging the cluster of sign candidates and the results of merging the clusters of the sign candidates.
Distance=∣x–xi∣+∣y−yi∣E14
C. Achromatic Decomposition
The key feature of the rectangular speed limit road signs in USA is that all the common speed limit signs are in gray-scale as in Figure 10(a-d). There also exits advisory speed limit signs on the freeway exits as in Figure 10(e-f). In order to detect the actual speed limit signs as in Figure 11, the achromatic gray scale color of the signs is extracted by the achromatic decomposition whereas the non-gray scale advisory speed signs are ignored from the further consideration.
Figure 10.
(a-d) the rectangular speed limits road signs in USA. (e-f) the advisory speed signs on freeway exits.
Figure 11.
The schematic of the RGB model and the angle α.
The vector of gray scale is along (1,1,1) in RGB color space and the inner product is between (1,1,1) and the each considered pixel checks an angle α between these two vectors to apply the decomposition in RGB domain [41] as illustrated in Figure 10(a-d).
Each of the considered pixel is in the vector form of (r, g, b). The cosine function of α, which is equal to the inner product [26] is shown in Eq. (15).
Cosα=111·rgb=r+g+b3×r2+g2+b2E15
For the implementation in our proposed, a mere value of cos2 is considered. If the value is near to one, α is near to zero, which implies the considered pixel is in gray-scale and is taken in account for the further steps. Figure 12 shows the results of the achromatic decomposition where the speed warning signs of non-gray scale found on the freeway exits are not acknowledged.
Figure 12.
The results of the achromatic decomposition.
D. Binarization
In the proposed system, the Otsu threshold method is used for the daylight binarization while the adaptive threshold method during the nighttime. Figure 13 illustrates these proposed steps.
Figure 13.
The proposed steps of binarization.
To differentiate between daylight and the night-light, the ROI is set to the cent er part of the frame choosing the width ROIw and height ROIh as in Eqs. (16) and (17) where the sky often lies. The noise with extremely high and low pixel values are filtered out and then in the remaining 75% of the pixels, the average of pixel values is calculated to judge if it is a day-light or a night condition. Figure 14 shows the schematic of the day and nighttime judgment.
Figure 14.
The schematic of the day and night judgment.
ROIh=Height of the frame6E16
ROI=23Width of the frame;ExcludingWidth of the frame6oneither endsE17
The Otsu method [42] can automatically decide the best threshold to binarize well in daytime, but at night, the chosen threshold causes the breakage of the sign digit.
In Otsu method, the best threshold that can divide the content into two groups and minimize the sum of variances in each group is found by an iterative process. Figure 15 shows the schematic steps of Otsu method. On the other hand, the adaptive threshold is more sensitive. It divides the signs into several sub-blocks, mean of each block is calculated, and then the threshold for respective sub-blocks is computed based on the means in each sub-block. The corresponding results of the adaptive threshold is as shown in Figure 16.
Figure 15.
The schematic steps of Otsu method.
Figure 16.
Different thresholding results.
Therefore, we chose Otsu threshold to automatically select the best-fit threshold in automatically daytime and adaptive threshold at night to handle the low contrast environment.
For acceleration, this paper adopts the integral image [36] in which each pixel is compared to an average of the surrounding pixels. An approximate moving average of the last s-pixels seen is specifically calculated while traversing the image. If the value of the current pixel is lower than the average then it is set to black, otherwise it is set to white. In the proposed algorithm, it is considered to be stored at each location, I(x, y), the sum of all f(x, y) terms to the left and above the pixel (x, y). This is accomplished in linear time using Eq. (18) for each pixel. Once the integral image is first calculated, the sum of the function for any rectangle with the upper left corner (x1, y1), and lower right corner (x2, y2) can be computed in constant time using Eq. (19). The schematic of Eq. (13) can be illustrated with Figure 17 and Eq. (19) can be modified into Eq. (20). Finally, the mean of each sub-block can be calculated and then each pixel in the sub-block which in terms of A(x, y) can be binarized with Eq. (21) where ∝ is a scalar based on the contrast under different conditions.
Connected component labelling (CCL) labels the object inside the sign candidates with the height, width, area and coordinate information. The CCL algorithm [28] is divided into two processing passes namely first pass and the second pass. Two different actions are taken in these passes if the pixel iterated is not the background.
The steps of connected component labeling are illustrated in Figure 18. In this case, the equivalent labels are (1, 2), (3, 7) and (4, 6).
Figure 18.
The steps for connected-component labeling, (a) processing initialization, (b) the result after the first pass, (c) the result after the second pass.
F. Digit Segmentation
The purpose for digit segmentation [43] is to extract the digit from the binarized image. In the process of rectangular speed limit signs detection, the signboards have the characters reading “SPEED LIMIT” alongside the digits. As a result, it is necessary to set constrains on size of the digit candidates as per Eqs. (22) and (23). Similarly, the constraints on the size of the digit candidates in circular speed limit signs are as per Eqs. (24) and (25).
0.15xW≤Digit width≤0.5xWE22
0.15xH≤Digit height≤0.5xHE23
0.125xR≤Digit width≤RE24
0.5xR≤Digit height≤1.5xHE25
Considering the fact that rectangular speed limit signs consist two-digits alongside the characters, it must be ensured that the selected candidates are of digits of speed limit sign and not the characters. The pairing rule of sizes and positions proposed in this paper are as follows:
The areas of the digit candidates should be similar.
The positons of the digit candidates should be closely packed.
The density of the pixels inside the digit candidates should be similar.
Whereas in the circular speed limit detection that are of both 2-digits and 3-digits, a loose constrain is adopted as it has only digits inside the circular speed limit signs and no characters. The pairing steps are similar to those followed in the rectangular speed limit signs. Figures 19 and 20 shows the segmentation results of the rectangular and circular speed limit signs, respectively.
Figure 19.
The example of digit segmentation results of rectangular speed limit signs.
Figure 20.
The example of digit segmentation results of circular speed limit signs.
There exists a critical challenge in binarization process, as the digits may appear connected to each other. To overcome this challenge, a two-pass segmentation process is proposed in this paper. Digit segmentation, similar to the previous segmentation process, is applied. If large components are detected, then the second pass of the segmentation is applied.
The horizontal pixel projection is applied in the second pass segmentation. During this projection, the total number of pixels in each column is accumulated choosing the segment line based on the horizontal projection. Figure 21 shows the example of horizontal projection and the result of the two-pass segment step.
Figure 21.
Example of horizontal projection and the result of the proposed two-pass segmentation steps.
G. Digit Recognition
In the digit recognition phase, the extracted digits are compared with the built-in templates and three probable digits with least matching difference are selected based on Sum of Absolute Difference (SAD) [44]. After which, the blob and breach features of the digits are applied to verify the final digits [45, 46, 47]. Figure 22 depicts the proposed steps for digit recognition.
Figure 22.
The proposed steps for digit recognition.
After selecting the three probable digit candidates, the blob feature is employed to verify the digit. Here the blob is defined as a close region inside the digit and is detected and gathered in several rows forming a union row. The pixel value of a union row is the union of values of the rows in that union row. For each union row, the number of lines in the white pixels are counted. A blob is formed only if the number of lines is of the sequence “1, 2,.…, 2, 1”. This union row method yields the position and the number of the blobs in the digit candidate as in Table 1. The exact blob feature is defined for the specific digits.
Digit
Blob feature verification
Breach feature verification
Number of blobs
Position of blobs
Number of breaches
Direction of breaches
0
One
Top to Bottom
Zero
—
1
Zero
—
Zero
None
2
Zero
—
Two
Top Left & Bottom Right
3
Zero
—
One
Left
4
One
Top Left
Zero
—
5
Zero
—
Two
Top Right & Bottom Left
6
One
Lower Half
One
Top Right
7
Zero
—
One
Left
8
Two
Upper & Lower Halves
Zero
—
9
One
Upper Half
One
Bottom Left
Table 1.
The blob and breach feature verification for all the digits.
Similarly, the breach feature is also adopted to verify the digits. A breach is defined as an open region formed by a close region with a gap. The breach is detected by counting the number of pixels where the white pixel first appears from both the right and the left in each column to half the width of the digit candidates. If there is a series of pixels that are larger than half the digit height, then it is regarded as a breach. Table 1 shows the blob and breach feature verification for the digits from 0 to 9 and Figure 23 shows the results of the digit recognition in terms of blob and breach feature verification.
Figure 23.
Digit recognition results.
H. FAST Feature Extraction
Features from Accelerated Segment Test (FAST) [48, 49] is a high repeatability corner detector. As shown in Figure 24, it uses a circle of 16 pixels to classify whether or not a candidate point p is a corner. The FAST feature extraction conditions can be written as in Eq. (26) where Sis a set of N contiguous pixels in the circle, Ix is intensity of x, Ip is intensity of candidate p and t is the threshold.
Figure 24.
The illustration of FAST algorithm.
∀x∈S,Ix>Ip+t,and∀x∈S,Ix<Ip−tE26
There are two parameters to be chosen in FAST algorithm namely, the number of contiguous pixels N and the threshold t. N is fixed as 9 in the proposed algorithm whereas to overcome the changes in the intensity inclinations as shown in Figure 25, the threshold t is set to be dynamic. The dynamic threshold is calculated by the image patch of the sign candidates. First, we count for pixels with intensity bigger than 128. If the number of bright pixels is between 20% and 80% of the total number of pixels in the image patch, the threshold is computed by the percentage of number of bright pixels over the total number of pixels. There are two fixed thresholds for the conditions that the number of bright pixels is lower than 20% of the total number of pixels or higher than 80% of the total number of pixels. Accordingly, the threshold dynamically updates to the number of bright pixels over the total number of pixels.
Figure 25.
Different lighting conditions of road signs.
I. Fixed Feature Extraction
There are certain conditions in which the contents of road signs are too simple to be extracted using the FAST feature, as shown in Figure 26(a-c). Thus, the Fixed Feature Extraction is applied to handle these road signs with good inclinations in the proposed algorithm.
Figure 26.
(a-c) Road signs with simple contents; (d) thirty fixed feature points used in fixed feature extraction.
Fixed Feature Extraction uses thirty fixed feature points to describe a road sign, as shown in Figure 26(d). This method is similar to template matching, but it is more robust to noises as it uses descriptors to describe the small area around the feature points.
J. Feature Matching
The main objective of this phase is to match the features between the pre-built template and the detected sign candidates as shown in Figure 27. The features extracted previously are matched by their coordinates and the descriptors which are constructed to describe their respective features. Due to the fact that the proposed system is aimed at real-time applications, the construction and the matching procedure of the descriptor algorithm should be both simple and efficient at the same time.
Figure 27.
Steps followed in the feature matching.
Binary Robust Independent Elementary Features (BRIEF) [50] is a simple descriptor with good matching performance and low computation cost. In order to build a BRIEF descriptor of length n,npairs (xi,yi) are chosen. X and Y representing the vectors of point xi and yi respectively, are randomly sampled with the Gaussian distribution and stored in a pre-built array to reduce the computation cost. They are sampled with Gaussian distribution stored in a pre-built array to reduce computation cost. To build a BRIEF descriptor, τ test is defined as in Eq. (27) and n is chosen as 256 to yield the best performance.
τpxy≔1,px<py0,px≥pyE27
The advantages of BRIEF are obviously, low computation time and better matching performance whereas the disadvantage is that the BRIEF is not rotation invariant and scale invariant. Since the size of the detected signs is fixed and the road signs would not have too much rotation effect, these disadvantages do not influence the recognition result.
After descriptor construction, a two-step matching process comprising distance matching and descriptor matching is applied to match the detected sign candidates with the pre-built templates. Distance matching considers only the coordinates of the feature points. In this application of road signs recognition, the detected road signs should be a regular triangle sometimes with certain defects such as, lighting changes, slight rotation, and occluded with an object. Thus, the two similar feature points are not matched if the coordinates of these two feature points are different.
The goal of descriptor matching is to compute the distance between two descriptors, one is from the detected sign candidate and the other is from the pre-built template. As with all the binary descriptors, the measure of BRIEF distance is the number of different bits between two binary strings which can also be computed as the sum of the XOR operation between the strings.
After all sign candidates are matched, the scores comparison is applied to choose which template is the most suitable for final recognition result. The template with the scores higher than the others is judged as the result of the template matching. Moreover, the same result should be recognized a few times in several frames of a video to make sure that the result does not produce a false alarm.
The performance of the aforementioned DIP based algorithms in detecting and recognizing the road signs are discussed in the Section 3.
2.1.2 Computer neural network (CNN) methods to detect and recognize road signs
Artificial Neural networks (ANN) generally referred as Neural networks (NN), specifically as Computer Neural Networks (CNN) have been a sensation in the field of CV. ANNs, Artificial Intelligence (AI) and Deep Learning (DL) are interdependent and importantly, indispensable topics of recent years research and applications in engineering and in the technology industry. This reason for this prominence is that they currently provide the best solutions to many problems extensively in image recognition, speech recognition and natural language processing (NLP).
The inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen defines a neural network as “…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.” In simpler words, the theme of ANNs are motivated by biological neural networks to learn and process the information fed to it. Figure 28 shows the similarity in function of a biological neuron in Figure 28(a) with its respective mathematical model in Figure 28(b).
Figure 28.
(a) A representative biological neuron with (b) its mathematical model from [34].
A neuron is the fundamental unit of computation in a biological neural network whereas the basic unit of an ANN is called a node or a unit. The node/unit receives inputs from external sources and from other nodes within the ANN to process and computes an output. Every input has a characteristic weight (w) allotted based on its corresponding importance to other inputs. The node applies a function to the weighted sum of its inputs.
ANNs are generally organized in layers that are made up of numerous interconnected ‘nodes’ comprising an ‘activation function’ as in Figure 29. The inputs are presented to the ANN via the ‘input layers’, which communicates with one or more ‘hidden layers’ in which a particular processing is done by a system of weighted ‘connections’. The hidden layers then link to an ‘output layer’ where the answer is output. For the general model of ANN in Figure 29, the net input can be calculated as in Eq. (28) and the output by applying the activation function over the net input can be calculated using Eq. (29).
Recent researches and publications present that ANNs are extensively used for various applications ranging from object detections to learning to paint, to create images from the sentences, to play board games-AlphaGo and so on. There are many more unthinkable and unconvincing things done by the ANNs in the present days and research studies on further advancing them are going on rigorously.
There exist a various models presented from researches across the word for different applications. Some of the prominent CNNs are Single Shot Detector (SSD) [51], Region based Connect Neural Network (R-CNN) [52], Fast-RCNN [53], Faster-RCNN [54], You Only Look Once (YOLO) [55] and different versions of it, Generative Adversarial Networks (GAN) [56] and different modules [57] based on it and many more. This chapter also discusses CSPJacinto-SSD based on CSPNet [58] features in JacintoNet [59]. These innumerous ANNs are extensively used by the researchers and industries alike. The researchers and industries go hand-in-hand to investigate on further improvisations of the existing NNs, expanding them into diversified applications and solving a problem/challenge using effective and low-cost measures, ultimately manufacturing commercial products to make human lives easier and smarter.
In this chapter, we explore object detection NNs such as SSD, Faster-RCNN, YOLO and propose the newer CNN model termed as ‘CSPJacinto-SSD’ for the detection and recognition of road signs.
The SSD, as its name suggests, only need to take one single shot to detect multiple objects within the image. It has two components- a backbone and SSD head. The backbone model is a network used for pre-trained image classification. The SSD head is network with one or more convolutional layers added to the backbone network and the outputs are interpreted as the bounding boxes and classes of objects in the spatial location of the final layers activations as in Figure 30.
Figure 30.
SSD model adds several feature layers to the end of a base network, which predict the offsets to default boxes of different scales and aspect ratios and their associated confidences.
Faster-RCNN [54] comprises of two modules of which the first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector [53] that uses the proposed regions. The earlier version of the Faster-RCNN such as R-CNN and Fast R-CNN both use selective search method to find out the region proposals. The selective search method is a slow, hence time-consuming process affecting the performance of the network. To overcome these challenges, an advanced version of the R-CNN called Faster-RCNN was proposed [54] that has an object detection algorithm eliminating the selective search algorithm and the network learn the region proposals. Figure 31 shows a Faster-RCNN network.
Figure 31.
A single, unified faster R-CNN for object detection.
All of the previous object detection algorithms until the year 2015, used regions to locate the objects in the image. That means the network does not look at the complete image but only parts of the image that may the high probabilities of containing an object. In 2015, J. Redmon proposed a new NN called YOLO (You Only Look Once) [55] as in Figure 32. It is an object detection algorithm much different from the region-based algorithms. In YOLO, a single convolutional network predicts the bounding boxes and the class probabilities for these boxes.
Figure 32.
A representative of the YOLO architecture for object detection.
The overall architecture of CSPJacinto-SSD is shown in the Figure 33. The CSPNet [58] features are added in JacintoNet [59], which is a simple light-weighted model composed of convolution, group convolution, and max-pooling layers. The Cross Stage Partial (CSP) feature is proved to improve the accuracy while reducing the model parameters and complexity. The function of CSP is simply to split the feature maps into two parts along channels at the input of each stage, one part sends into the convolution block, as usual, the other part skips all layers and concatenate with the output convolution block together as the final block output. In Figure 33, one blue and one green square can be seen as a convolution block. The blue arrows show the CSP feature as described above, and the red arrows show the output of each stage. The 1 x 1 convolution before the convolution block is used to increase the feature channels, and the 1 x 1 convolution after the convolution block is used to merge the context of features from the CSP layer. Out1 to Out5 labels the feature maps that are used for dense heads to process the bounding box outputs.
Figure 33.
CSPJacinto-SSD model architecture.
The dense heads employed in the proposed CSPJacinto-SSD are referred to those in SSD, with some modifications in the anchor boxes based on the concept of multi-head SSD proposed in [60]. At dense head levels 2 to 4, there is an extra location of anchor boxes with offset 0, instead of only the original offset 0.5. This feature can increase the anchor boxes density improving the recall of object detection, especially used for light-weighted SSD models that need more anchor boxes to guide the objects’ possibly appeared location.
The anchor box settings are a bit different from the original SSD model. The anchors 1:2 is changed to 1:1.5 because it may make the anchor borders denser, and preserve 1:3 anchors. The base size of the anchor boxes are modified compared to the original SSD, as shown in Table 2. Those anchor sizes can better fit with our model input size 256x256.
Base sizes
Original SSD
16, 32, 64, 100, 300
Proposed CSPJacinto-SSD
16, 32, 64, 128, 256
Table 2.
Base size of anchor box.
The performance of these ANN object detection networks in detecting and recognizing the road signs are discussed in the Section 3.
3. Results and discussion
3.1 The DIP based algorithms
The DIP based algorithms for detecting and recognizing road signs are implemented in C++ on a Visual Studio platform on desktop computer and a Freescale i.MX6. Due to the lack of standard video datasets dedicated to speed limit signs, we have examined the algorithm using the original video frames captured under different weather conditions such as daylight, backlight, cloudy, night, rain and snow.
3.1.1 System specifications
The DIP based algorithm discussed in Section 2.1 for speed limit and speed regulatory signs are realized on the standard desktop machine consisting of Intel® Core™ i7–3770 CPU, operating at 3.6GHz frequency with a memory of DDR3–1600-8GB on a Windows-7 64 bit and Ubuntu 14.04. The same DIP based algorithms are also realized on the Freescale i.MX 6 which is one of the standard developing processors for the real-time vehicular applications with a ARM Cortex-A9 CPU possessing an operating frequency of 1.2 GHz and memory of 1GB working with a Linux operation system. The same has Video Processing Unit (VPU) decoder-H.264, MPEG-4, H.263, MJPE and Image Processing Unit (IPU) possessing blending, rotating, scaling, cropping, de-interlacing, color spacing converting functions.
3.1.2 Performance: speed
On the desktop computer as well as on the Freescale i.MX 6, the size of images of speed limit signs is set to D1 resolution (720x480). The processing speed of the DIP based algorithm could reach up to 150fps on an average on the desktop computer and about 30fps on the Freescale i.MX 6. The image resolution is set as 1280x720 on the desktop computer and the performance can reach 161 fps on an average. On Freescale i.MX 6, the image resolution is also set as 1280x720 and the performance of the proposed algorithm is 17 fps.
3.1.3 Performance: accuracy and comparison
The performance accuracy of rectangular and circular speed limit signs and triangular speed regulatory signs’ detection and recognition by DIP based algorithm discussed in Section 2.1 is tabulated in Table 3. The overall accuracy is defined as “when a car passes a road scene with a road sign instance, the final output of the proposed algorithm is correct as that of the road sign visible to naked eyes.”
Rectangular speed limit road signs
Circular speed limit road signs
Triangular speed regulatory road signs
Video Resolution
720x480
720x480
1280x720
Total Video Frames
13187
14332
227445
Total Road Signs Count
77
113
60
Detected Signs
74
108
59
Detection Accuracy
96.10%
95.58%
98.33%
Total Detected Signs Frame Count
429
697
902
Total Detected Signs and Correctly Classified Frames
The accuracies of the speed limit signs and speed regulatory signs detection and recognition.
The detection accuracy of rectangular speed-limit road signs is 96.10% and recognition accuracy is 97.30% accounting to the total accuracy of 93.51%. The detection accuracy of circular speed limit road signs is 95.58% and its recognition accuracy is 96.30% accounting to the overall accuracy of 91.15% whereas the detection accuracy of the triangular speed regulatory signs is 98.33% and the recognition accuracy is 94.92% resulting in the overall accuracy of 93.33%. The performance efficiency of these algorithms are evaluated under different weather conditions such as daytime, cloudy, with strong backlight, nighttime and during snow and rain. Some of these results are tabulated in Tables 4–6.
Video Sequence Number
1
2
3
4
5
6
7
8
Weather
Day
Day
Day
Day
Cloudy
Rain
Night
Night
Number of Signs
4
4
3
4
1
3
2
2
Detected signs
4
4
3
3
1
3
2
2
Missed Signs
0
0
0
1
0
0
0
0
Number of Frames with Sign Detection
15
17
10
9
4
12
7
9
Number of Correct Speed Limit Sign Recognition
15
16
10
8
3
12
6
9
Number of Wrong Speed Limit Sign Recognition
0
1
0
1
1
0
1
0
Table 4.
Some details of the rectangular speed limit road signs detection and recognition.
Video Sequence Number
1
2
3
4
5
6
7
8
9
10
Weather
Day
Day
Day
Day
Backlight
Cloudy
Snow
Rain
Night
Night
Number of Signs
4
6
5
5
5
4
3
2
2
2
Detected signs
4
6
5
5
5
4
2
2
1
2
Missed Signs
0
0
0
0
0
0
1
0
0
0
Number of Frames with Sign Detection
12
20
15
16
16
12
10
10
4
9
Number of Correct Speed Limit Sign Recognition
12
19
15
15
14
11
9
8
4
9
Number of Wrong Speed Limit Sign Recognition
0
1
0
1
2
1
1
2
0
0
Table 5.
Some details of the circular speed limit road signs detection and recognition.
Video Sequence Number
1
2
3
4
5
6
7
Weather
Day
Day
Day
Cloudy
Backlight
Night
Night
Number of Signs
6
3
5
2
4
3
4
Detected signs
5
3
5
2
3
2
4
Missed Signs
1
0
0
0
1
1
0
Number of Frames with Sign Detection
22
13
15
7
14
7
18
Number of Correct Speed Regulatory Sign Recognition
19
12
15
5
14
6
18
Number of Wrong Speed Limit Sign Recognition
3
1
0
2
0
1
0
Table 6.
Some details of the triangular speed regulatory road signs detection and recognition.
The efficiency of the proposed algorithm is also compared with the state-of-the-art works on the road signs detection and recognition.
As listed in Table 7, the proposed speed limit signs detection and recognition system is compared with some of the previous works. It can be implemented on embedded systems for real-time ADAS applications as it is capable of performing under computing resource and support both circular and rectangular speed limit road signs such as 15, 20, 25, 30, 35,….., 110 irrespective of the digit fonts from numerous countries adopting to the blob and breach features.
1.167GHz Intel Atom 230 and NVIDIA GeForce 9400 M GS GPU
2.26 GHz dual-core laptop
—
Intel Core i7–3770 3.40 GHz ARM Cortex-A9 1.2 GHz
Video Resolution
700 X 400
Image only
640 X 480
640 X 480
640 X 480
1920 X 1080
720 X 480
Frame Rate on PC
25fps
7.7fps (130 ms)
20fps
33fps
16fps
—
150fps
Detection Accuracy
87.00%
89.68%
97.50%
—
98.90%
—
95.84%
Recognition Accuracy
90.90%
88.97%
96.25%
95.00%
88.50%
96.24%
96.80%
Overall Accuracy
96.25%
90.90%
90.00%
88.00%
98.30%
94.00%
92.10%
Real-Time on Embedded System
X
X
X
X
X
X
O
Supports all types of Speed Limit Signs
X
X
X
X
X
X
O
Supports Different Digit Fonts on Speed Limit Signs
X
X
X
X
X
X
O
Table 7.
The comparison of the proposed speed regulatory road signs detection and recognition algorithm with previous works.
Table 8 lists the comparison of the proposed speed regulatory signs detection and recognition system with relative previous works. It can also be implemented on the embedded systems for real-time ADAS applications, as it is capable of performing under embedded computing resource and support different types of speed regulatory signs as in Figure 34 from numerous countries adopting feature extraction and feature matching features.
The comparison of the proposed speed regulatory road signs detection and recognition algorithm with previous works.
Figure 34.
Some samples of speed regulatory road signs.
From the comparison listed in the Tables 7 and 8, it can be interpreted that DIP based algorithm discussed in this chapter is more robust i.e., it supports speed limit and speed-regulatory road signs of different types existing in most of the countries. It also performs well with the varied fonts of the speed limit signs and it can be compatibly implemented in an embedded system for the real-time applications. Importantly, it has decent accuracy when working with video as in the real camcorder environment compared to the state-of-the-art methods. Above all, the least complexity of our proposed algorithm yields higher fps compared to those of the other previous works.
Figure 35 shows the experimental results of the speed limit road signs detection and recognition method for detection and recognition of the rectangular speed limit road signs. Figure 35(a-c) is the result during the daytime, Figure 35(d) is during the cloudy weather, Figure 35(e-f) is during the rain and Figure 35(g-j) is during the nighttime.
Figure 35.
The overall results for rectangular speed limit signs detection (a-c) during the daytime (d) during the cloudy weather (e-f) during the rain (g-j) during the nighttime.
Figure 36 shows the experimental results of the speed limit road signs detection method for detection as well as recognition of the circular speed limit road signs. Figure 36(a-c) is the result during the daytime, Figure 36(d-e) is during the backlight condition, Figure 36(f-g) is during the cloudy weather, Figure 36(h-i) is during the snow, Figure 36(j-l) is during the rains and Figure 36(m-o) is during the nighttime.
Figure 36.
The overall results for circular speed limit signs detection (a-c) during the daytime, (d-e) during the backlight condition, (f-g) during the cloudy weather, (h-i) during the snow (j-l) during rains, (m-o) during nighttime.
Figure 37 shows the experimental results of the speed regulatory road signs detection and recognition method for detection of the triangular speed regulatory road signs of which Figure 37(a-c) is the result during the daytime, Figure 37(d-g) is during the cloudy weather, Figure 37(h-i) is during the cloudy weather and Figure 37(j-l) is during the nighttime.
Figure 37.
The overall results for triangular speed regulatory road signs detection (a-c) during the daytime, (d-g) during the backlight condition, (h-i) during the cloudy weather (j-l) during the night.
Additionally, the proposed CV based method is capable of detecting and recognizing the speed limit signs ending with the digit “5” as in Figure 38.
Figure 38.
The detection and recognition result of speed limit ending with the digit ‘5’.
3.2 The CNN based algorithms
The CNN based object detection algorithms such as SSD [51], Faster R-CNN [54], YOLO [55] and the proposed CSPJacinto-SSD are implemented in Python. In order to carry out the roads signs detection and recognition, the same are trained and tested using a traffic signs dedicated dataset titled ‘Tsinghua-Tencent 100 K’ [9].
3.2.1 System specifications
The CNN based algorithm discussed in Section 2.1.2 for road signs are realized on the standard desktop machine consisting of Intel® Core™ i7–3770 CPU, operating at 4.2GHz frequency with a memory of DDR3–1600-16GB on a Windows-10 64 bit with Geforce GTX Titan X.
3.2.2 Performance: speed
On the desktop computer, the size of images are used as available in the dataset. The processing speed of the SSD, Faster-RCNN, YOLO and CSPJacinto-SSD object detection algorithms are around 20 fps, 5 fps, 21 fps, and 22 fps respectively.
3.2.3 Performance: accuracy and comparison
The performance efficiency of the CNN models is mostly calculated using mAP, AP and IoU as per [61]. Average precision (AP) is the most commonly used metric to measure the accuracy of object detection by various CNNs, and image-processing methods. The AP computes the average precision value for recall value. Precision measures how accurate the predictions are, by a method, i.e., the percentage of correct predictions whereas, Recall measures the extent to which the predicted positives are good. Eq. (30) is employed in this paper to estimate the AP where r refers to recall rate and r̂ refers to the precision value for recall. The interpolated average precision [62] was used to evaluate both classification and detection. The intention in interpolating the precision/recall curve in this way is to reduce the impact of the wiggles in the precision/recall values, caused by small variations in the ranking. Similarly, the mean average precision (mAP) is the average of AP. The accuracy of these models [51, 54, 55] in detecting and recognizing road signs from dataset [9] is as tabulated in Table 9.
Input resolution
mAP
FPS
Complexity per frame
No. of parameters
SSD 512
512 x 512
67.90%
∼20
105.30G
42.40 M
Faster RCNN
608 x 608
75.20%
∼5
120.60G
41.72 M
YOLO V4
608 x 608
71.40%
∼21
64.40G
64.50 M
CSPJacinto-SSD
512 x 512
69.60%
∼22
18.80G
11.40 M
Table 9.
Performance efficiency of CNN models in detection and recognition of road signs.
The traditional DIP based methods are popular CV techniques namely SIFT, SURF, BRIEF, to list a few employed for object detection. Feature extraction process was carried out for image classification jobs. The features are descriptive of “interesting” in images. Various CV algorithms, such as edge detection, corner detection and/or threshold segmentation would be involved in this step. Thus extracted features from images forms the basis for definition for an object to be detected for respective class. During deployment of such algorithms, these definitions are sought in other images. If a significant number of features from defined for a class are found in other images, the image is classified, respectively.
Contrastingly, CNN came up with end-to-end learning where a machine learns about the object from a classes of annotated images which is termed as ‘training’ of a given dataset. During this, the CNN perceives the fundamental patterns in those classes of images and consistently establishes a descriptive salient features for each specific classes of objects.
With almost all the researches and industries that involves CV are now employing CNN based methods, the functionalities of a CV professional has exceptionally changed in terms of both knowledge, skills and expertise as in Figure 39.
Figure 39.
Comparison of DIP and CNN based workflow. Fig. From [63].
A comparison between the DIP and CNN based methods is tabulated in Table 10. The DIP based methods are more compatible and suited for the real-time applications of the ADAS as it is of low-complexity and does not require any data for pre-training of the system as compared to the data-hungry neural networks based systems. Apart from pre-training and complexity, the Freescale iMX6 consumes a total power of 1.17 W [64] in the video playback idle mode and other embedded systems would lie in the range where a minimum of 300 W [65] required by a basic GPU which makes them power-hungry as well. Additionally, the DIP based methods can perform object detection in any scenes irrespective of having seen the same or similar scene, but the CNN models can perform object detection of the objects that they models have seen during the training processes. Hence, the CNN models require good-amount of time spent to teach them perform a task followed by testing before employing them for the real-time applications unlike the DIP based methods. Moreover, CNN models exhibit high flexibility and perform better in inclement weathers than DIP based methods.
Parameters
Proposed system
CNN based systems
Complexity
Low
High
Pre-training
X
O
Power Consumption
1.17 W
300 W
Recognition in scene never seen before
O
X
Robustness to inclement weathers
X
O
Table 10.
Comparison of the proposed system with that of CNN based systems.
5. The conclusion
This chapter discussed traditional image processing methods and a few CNN based methods to detection and recognition of road signs for ADAS systems. It has been conclusive that DNNs perform better than the traditional algorithms with certain specific trade-offs with respect to computing requirements and training time. While there are pros and cons of both traditional DIP and CNN based methods, a lot of DIP based CV methods invented over the last 2–3 decades have now become obsolete because newer and much more efficinet methods of CNN have replaced them. However, knowledge and skills gained are also invaluable and not bounded by never inventions instead the knowledge of traditional methods forms a strong foundation for the professional to explore and widen his point-of-viewing a problems. Additionally, there are some of the traditional methods still being used in a hybrid-approach to improvise, innvoate leading to incredible innovations.
Acknowledgments
The authors thank the partial support by the “Center for mmWave Smart Radar Systems and Technologies” under the “Featured Areas Research Center Program” within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taiwan R.O.C. We also thank the partial support from the Ministry of Science and Technology (MOST), Taiwan R.O.C. projects with grants MOST 108-3017-F-009-001, MOST 110-2221-E-A49-145-MY3, and MOST 109-2634-F-009-017 through Pervasive Artificial Intelligence Research Labs (PAIR Labs) in Taiwan, R.O.C. as well as the partial support from the Qualcomm Technologies under the research collaboration agreement 408929.
Conflict of interest
The authors declare no conflict of interest.
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R.",surname:"José Ribeiro Neves",slug:"antonio-jose-ribeiro-neves",fullName:"António José Ribeiro Neves"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Computer vision in ADAS applications",level:"1"},{id:"sec_2_2",title:"2.1 Road signs detection and recognition",level:"2"},{id:"sec_2_3",title:"Table 1.",level:"3"},{id:"sec_3_3",title:"Table 2.",level:"3"},{id:"sec_6",title:"3. Results and discussion",level:"1"},{id:"sec_6_2",title:"3.1 The DIP based algorithms",level:"2"},{id:"sec_6_3",title:"3.1.1 System specifications",level:"3"},{id:"sec_7_3",title:"3.1.2 Performance: speed",level:"3"},{id:"sec_8_3",title:"Table 3.",level:"3"},{id:"sec_10_2",title:"3.2 The CNN based algorithms",level:"2"},{id:"sec_10_3",title:"3.2.1 System specifications",level:"3"},{id:"sec_11_3",title:"3.2.2 Performance: speed",level:"3"},{id:"sec_12_3",title:"Table 9.",level:"3"},{id:"sec_15",title:"4. The comparison of DIP and CNN based methods",level:"1"},{id:"sec_16",title:"5. The conclusion",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"},{id:"sec_20",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'J. Urry, “The ‘System’ of Automobility”, Theory, Culture & Society, vol. 21, no. 4-5, pp. 25-39, October 2004.'},{id:"B2",body:'E. 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J Manuf Syst 48:144–156. https://doi.org/10.1016/J.JMSY.2018.01.003'},{id:"B64",body:'Freescale Semiconductor, “i.MX 6Dual/6Quad Power Consumption Measurement” from “https://bit.ly/2ATVcWk”'},{id:"B65",body:'GEFORCE. “Desktop GPUs-Specifications” from “https://www.geforce.co.uk/hardware/desktop-gpus/geforce-gt-1030/specifications”'}],footnotes:[{id:"fn1",explanation:"Machine learning (ML) is a branch of artificial intelligence (AI) and computer science, which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy [4]."},{id:"fn2",explanation:"Digital Image Processing (DIP) refers to the use of computer algorithms to perform image processing on digital or digitized images, leading to the extraction of attributes from the processed images and to the recognition and mapping of individual objects, features or patterns [5]."},{id:"fn3",explanation:"An Artificial Neural Network (ANN) is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates [6]."},{id:"fn4",explanation:"Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving [7]."}],contributors:[{corresp:"yes",contributorFullName:"Vinay M. Shivanna",address:"vinay.ms23@gmail.com",affiliation:'
Department of Electronics Engineering and Institute of Electronics, National Yang Ming Chiao Tung University, Taiwan
Department of Electronics Engineering and Institute of Electronics, National Yang Ming Chiao Tung University, Taiwan
Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan
Wistron-NCTU Embedded Artificial Intelligence Research Center, Taiwan
'}],corrections:null},book:{id:"10652",type:"book",title:"Information Extraction and Object Tracking in Digital Video",subtitle:null,fullTitle:"Information Extraction and Object Tracking in Digital Video",slug:null,publishedDate:null,bookSignature:"Prof. António J. R. José Ribeiro Neves and Dr. Francisco Javier Gallegos-Funes",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-461-5",printIsbn:"978-1-83969-460-8",pdfIsbn:"978-1-83969-462-2",isAvailableForWebshopOrdering:!0,editors:[{id:"1177",title:"Prof.",name:"António",middleName:"J. 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It is also grown in many countries of the world. The crop has been facing numerous biotic and abiotic constraints. Among biotic constraint crop affected adversely by diseases, caused by many pathogens. Ever since 1918 when for the first time wilt disease of chickpea was reported and Fusarium oxysporum f. sp. ciceri was the causal organism many strategies have been adopted to control the wilt disease. The controlling methods included conventional as well as modern one. However, more and more emphasis was given on biological control agents such as AM fungi and Trichoderma. The role of AM fungi have been evaluated for controlling the wilt disease similarly role of Trichoderma is thoroughly established biological control agent against Fusarium wilt. With the advent of modern tools and techniques developing markers, resistant varieties, all such sources enable us to reduce the effect of pathogens. Here an attempted has been made to acknowledge the trend of disease management and evaluation strategies of Fusarium wilt of chickpea for getting better yields of the crop.",signatures:"Chandan Singh and Deepak Vyas",authors:[{id:"315463",title:"Mr.",name:"Chandan",surname:"Singh",fullName:"Chandan Singh",slug:"chandan-singh",email:"chandan.singhx20@gmail.com"},{id:"338974",title:"Prof.",name:"Deepak",surname:"Vyas",fullName:"Deepak Vyas",slug:"deepak-vyas",email:"deepaknvyas64@gmail.com"}],book:{id:"10113",title:"Diagnostics of Plant Diseases",slug:"diagnostics-of-plant-diseases",productType:{id:"1",title:"Edited Volume"}}},{id:"77543",title:"Biodegradation by Fungi for Humans and Plants Nutrition",slug:"biodegradation-by-fungi-for-humans-and-plants-nutrition",abstract:"Fungi being achlorophyllous depends on other living organisms for their food either being parasite or saprophyte. Saprophytic fungi are good biodegraders. Through their enzymatic batteries, they can degrade any organic substances. Most of the time during the processes of degradation, macrofungi (mushrooms) are occurred as per the climatic conditions prevailing in the particular locations. Micro and macrofungi are considered a good source of human nutrition and medicine since time immemorial. Some of the fungi which are commonly known as mycorrhizae facilitate nutrients to more than 90% of green plants. Fungi play a basic role in plant physiology and help in the biosynthesis of different plant hormones that provides the flexibility of plant to withstand adverse environmental stress, the whole fungi are more friend than foe.",signatures:"Chandan Singh and Deepak Vyas",authors:[{id:"315463",title:"Mr.",name:"Chandan",surname:"Singh",fullName:"Chandan Singh",slug:"chandan-singh",email:"chandan.singhx20@gmail.com"},{id:"338974",title:"Prof.",name:"Deepak",surname:"Vyas",fullName:"Deepak Vyas",slug:"deepak-vyas",email:"deepaknvyas64@gmail.com"}],book:{id:"10681",title:"Biodegradation Technology of Organic and Inorganic Pollutants",slug:"biodegradation-technology-of-organic-and-inorganic-pollutants",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"249274",title:"Dr.",name:"Amin U.",surname:"Mridha",slug:"amin-u.-mridha",fullName:"Amin U. Mridha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Chittagong",institutionURL:null,country:{name:"Bangladesh"}}},{id:"282316",title:"Prof.",name:"Xin-Gen",surname:"Zhou",slug:"xin-gen-zhou",fullName:"Xin-Gen Zhou",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"315463",title:"Mr.",name:"Chandan",surname:"Singh",slug:"chandan-singh",fullName:"Chandan Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/315463/images/10927_n.jpg",biography:"Working in organic compost sustainable agriculture, Plant protection, Biochar, and Mushroom",institutionString:null,institution:null},{id:"326774",title:"Prof.",name:"Yong",surname:"Wang",slug:"yong-wang",fullName:"Yong Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"326775",title:"Dr.",name:"Monika",surname:"Dayarathne",slug:"monika-dayarathne",fullName:"Monika Dayarathne",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Guizhou University",institutionURL:null,country:{name:"China"}}},{id:"330269",title:"Dr.",name:"Eeshan",surname:"Kalita",slug:"eeshan-kalita",fullName:"Eeshan Kalita",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"331212",title:"Prof.",name:"Dong-Yan",surname:"Zhang",slug:"dong-yan-zhang",fullName:"Dong-Yan Zhang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"341484",title:"Ms.",name:"Chayanika",surname:"Chaliha",slug:"chayanika-chaliha",fullName:"Chayanika Chaliha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Tezpur University",institutionURL:null,country:{name:"India"}}},{id:"343541",title:"Dr.",name:"Fenfang",surname:"Lin",slug:"fenfang-lin",fullName:"Fenfang Lin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Nanjing University of Information Science and Technology",institutionURL:null,country:{name:"China"}}},{id:"357459",title:"Dr.",name:"Yu",surname:"Lei",slug:"yu-lei",fullName:"Yu Lei",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding-funders-list",title:"List of Funders by Country",intro:"
If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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IMPORTANT: You must be a member or grantee of the listed funders in order to apply for their Open Access publication funds. Do not attempt to contact the funders if this is not the case.
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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Quina, Fabio Gozzi, Volnir O. Silva, Leidi C. Friedrich and José E. F. Moraes",authors:[{id:"85138",title:"Dr.",name:"Amilcar",middleName:null,surname:"Machulek Jr.",slug:"amilcar-machulek-jr.",fullName:"Amilcar Machulek Jr."},{id:"95179",title:"Prof.",name:"Frank",middleName:null,surname:"Herbert Quina",slug:"frank-herbert-quina",fullName:"Frank Herbert Quina"},{id:"95183",title:"MSc.",name:"Fabio",middleName:null,surname:"Gozzi",slug:"fabio-gozzi",fullName:"Fabio Gozzi"},{id:"95189",title:"Dr.",name:"Volnir",middleName:null,surname:"O. Silva",slug:"volnir-o.-silva",fullName:"Volnir O. Silva"},{id:"95193",title:"BSc.",name:"Leidi",middleName:null,surname:"C. Friedrich",slug:"leidi-c.-friedrich",fullName:"Leidi C. Friedrich"},{id:"95197",title:"Prof.",name:"José",middleName:null,surname:"E. F. Moraes",slug:"jose-e.-f.-moraes",fullName:"José E. F. 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Climate change occurs as a result of an imbalance between incoming and outgoing radiation in the atmosphere. The global mean temperatures may increase up to 5.4°C by 2100. Climate change is mainly caused by humans, especially through increased greenhouse gas emissions. Climate change is recognized as a serious threat to ecosystem, biodiversity, and health. It is associated with alterations in the physical environment of the planet Earth. Climate change affects life around the globe. It impacts plants and animals, with consequences for the survival of the species. In humans, climate change has multiple deleterious consequences. Climate change creates water and food insecurity, increased morbidity/mortality, and population movement. Vulnerable populations (e.g., children, elderly, indigenous, and poor) are disproportionately affected. Personalized adaptation to the consequences of climate change and preventive measures are key challenges for the society. Policymakers must implement the appropriate strategies, especially in the vulnerable populations.",book:{id:"9664",slug:"environmental-issues-and-sustainable-development",title:"Environmental Issues and Sustainable Development",fullTitle:"Environmental Issues and Sustainable Development"},signatures:"Hassan M. Heshmati",authors:[{id:"313921",title:"Dr.",name:"Hassan M.",middleName:null,surname:"Heshmati",slug:"hassan-m.-heshmati",fullName:"Hassan M. 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As a result, the optimal formula is obtained with 300 g of mycorrhizal and rhizobium strains + 500 g of black soil + 200 g of potato peel crust, which has an effective antagonistic capacity of 100% in pea cultivation, 90% in the barley, and 85% in the potato, besides that it achieves a biotisation in the cultivation of peas of 95%, in the barley 100% and in the potato 90%.",book:{id:"11177",title:"Biomass, Biorefineries and Bioeconomy",coverURL:"https://cdn.intechopen.com/books/images_new/11177.jpg"},signatures:"Henry Juan Javier Ninahuaman and Grimaldo Quispe Santivañez"},{id:"82542",title:"Effects of Veld Degradation on Biomass Production in the Arable Lands of South Africa",slug:"effects-of-veld-degradation-on-biomass-production-in-the-arable-lands-of-south-africa",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.102605",abstract:"This paper reviews the impacts of veld degradation on species diversity, veld ecological condition. The major focus of this review is to assess the major critical factors that contributeto veld degradation. It is imperative to revitalize information on the effects of veld degradation in the South African pastoral farming systems. Current studies have indicated the limited research gaps that identify the adverse effects of veld degradation on species composition and biomass production. Grazing behavior in different grazing patterns has not been clear. Finally, this review will assist farmers, policymakers, and pastoralists to broaden their knowledge on policy development, and appropriate the veld management practices, coping measures of veld degradation, particularly those from resource-poor communities. Whereby, livestock production is the focus for food security and poverty alleviation. However, the use of legumes intercropped with temperate grass species can improve animal performance and herbage production during critical periods. The review further evaluates the veld management practices and their ability in providing adequate foliar cover with the use of the edible perennial grass plant that ensures long-term sustainable production with maximum economic returns during critical grazing seasons.",book:{id:"11177",title:"Biomass, Biorefineries and Bioeconomy",coverURL:"https://cdn.intechopen.com/books/images_new/11177.jpg"},signatures:"Nkosikhona Madolo and Francis B. Lewu"},{id:"82330",title:"Advances in Bioenergy Production Using Fast Pyrolysis and Hydrothermal Processing",slug:"advances-in-bioenergy-production-using-fast-pyrolysis-and-hydrothermal-processing",totalDownloads:7,totalDimensionsCites:0,doi:"10.5772/intechopen.105185",abstract:"This chapter provides an overview of current efforts and advances as well as environmental and economic aspects of fast pyrolysis and hydrothermal processing, which are potential technologies for bioenergy production, mainly bio-oil and syngas. Biomass is presently the primary bioenergy resource in the world. The chapter presents a brief discussion of sources and compositions of biomass. Biomass is converted to various products using thermochemical conversions. Pyrolysis is a thermochemical process that converts biomass into carbon-rich solid residue, condensable vapors, and non-condensable gases in the absence of oxygen. It is a promising technology for converting biomass into renewable biofuels with environmental and economic advantages. Pyrolysis processes are classified based on their operating conditions and desired products. Two thermochemical processes, fast pyrolysis and hydrothermal processing are reviewed. Fast pyrolysis produces a higher quantity and quality of bio-oil and syngas than slow and intermediate pyrolysis processes. Hydrothermal processing converts wet biomass into carbonaceous biofuel. The ability to produce higher-value bioenergy by these pyrolysis technologies depends on the feedstock and operating condition of the pyrolysis processes. This chapter will present the most promising features of fast pyrolysis and hydrothermal processing along with their optimal pyrolysis conditions in maximizing the production of biofuels.",book:{id:"11177",title:"Biomass, Biorefineries and Bioeconomy",coverURL:"https://cdn.intechopen.com/books/images_new/11177.jpg"},signatures:"Meegalla R. Chandraratne and Asfaw Gezae Daful"},{id:"81162",title:"Economic Assessment of Biomass Based Power Generation",slug:"economic-assessment-of-biomass-based-power-generation",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.103692",abstract:"Biomass based power generation systems can play a significant role to alleviate energy crisis and reduce fossil fuel dependency in the countries that possess abundance of agricultural and forest biomass resources. Particularly the countries to go for biomass energy in a large scale must know power and energy potential for biomass based commercial production with proper economic assessment of the possibilities. In-depth knowledge is must to assess the profitability and sustainability of the projects. Profitability measures how the investment in the project can be secured to have an ensured surplus to be shared by the stake holders and sustainability ensures the long-term existence in the business with a positive trend of gaining market share day by day or simply to be in the business. This chapter will present the details of the economic assessment of biomass- based energy projects in terms of net present value (NPV), internal rate of return (IRR), discounted payback period (DPB), and cost of energy. The economic profitability measure is a must before advancing to a venture whether it is self-financed or loan financed. So, it is hoped that readers of the chapter should develop a proper evaluation capability and know how to analyze the biomass-based energy projects.",book:{id:"11177",title:"Biomass, Biorefineries and Bioeconomy",coverURL:"https://cdn.intechopen.com/books/images_new/11177.jpg"},signatures:"A.B.M. Abdul Malek"},{id:"80542",title:"Comparative Analysis of Biodiesel Production from Different Potential Feedstocks in the Philippines",slug:"comparative-analysis-of-biodiesel-production-from-different-potential-feedstocks-in-the-philippines",totalDownloads:31,totalDimensionsCites:0,doi:"10.5772/intechopen.102724",abstract:"In response to the worsening crisis on energy security and climate change, the Philippine Biofuels Law (Republic Act 9367) was enacted which mandates the blending of biodiesel to petroleum diesel sold in the country. Primarily, feedstock and pricing concerns led to stagnant growth of the Philippine biodiesel industry. Hence, viability of different potential biodiesel feedstocks such as coconut, oil palm, and soybean (first generation), jatropha and used cooking oil (second generation), and microalgae (third generation) was assessed through extensive research and developments. Among these sources, oil palm is regarded as the best complementary feedstock to coconut due to its high biodiesel productivity of 376 million liters per year. Oil palm biodiesel production in the Philippines was also found to have a low carbon footprint of 1.80 kg CO2e per liter and a GHG reduction potential of 42%, which corresponds to a GHG savings of about 1.05 million metric tons CO2e per year for a 5% blending mandate in 2025. Additionally, a low biodiesel selling price of about Php 33.26 per liter can be achieved from using this feedstock for biodiesel production. Hence, use of a low cost and readily available feedstock coupled with established processing technologies and pricing mechanisms will help boost the biodiesel industry in the Philippines.",book:{id:"11177",title:"Biomass, Biorefineries and Bioeconomy",coverURL:"https://cdn.intechopen.com/books/images_new/11177.jpg"},signatures:"Rona Joyce B. Landoy, Rex B. Demafelis, Bernadette T. Magadia and Anna Elaine D. Matanguihan"},{id:"80493",title:"Conventional and Unconventional Transformation of Cocoa Pod Husks into Value-Added Products",slug:"conventional-and-unconventional-transformation-of-cocoa-pod-husks-into-value-added-products",totalDownloads:42,totalDimensionsCites:0,doi:"10.5772/intechopen.102606",abstract:"The drive for a sustainable society and a circular economy has motivated researchers around the globe to turn to the transformation of renewable raw materials like biomass into value-added products that are akin or superior to their fossil counterparts. Among these biomass raw materials, cocoa pod husks (CPH) which is the non-edible portion of cocoa (ca. 70–75% weight of the while cocoa fruit) remains a promising bio-resource raw material for the production high-value added chemicals but yet largely underexploited. Currently, the most popular applications of CPH involves its use as low-value application products such as animal feed, raw material for soap making, and activated carbon. However, the rich source of lignocellulosic content, pectin, and phenolic compounds of CPH means it could be used as raw materials for the production industrially relevant platform chemicals with high potential in the agrochemicals, pharmaceutical, and food industries, if efficient transformations routes are developed by scientists. In this chapter, we will shed light on some of the works related to the transformation of CPH into various value-added products. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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