",isbn:"978-1-80356-465-4",printIsbn:"978-1-80356-464-7",pdfIsbn:"978-1-80356-466-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"611776f7f3cc9951a8956d2e3d535a8e",bookSignature:"Associate Prof. Chatchawal Wongchoosuk",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11762.jpg",keywords:"Sensor, Boron Nitride, Energy Applications, Sensing Application, Borophene, 2D Boron Sheet, Boron Nitride Nano-Sheets, BNNS, Boron Carbide, Boron–Carbon Ceramic, B4C, Boron Doping",numberOfDownloads:0,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 2nd 2022",dateEndSecondStepPublish:"May 3rd 2022",dateEndThirdStepPublish:"July 2nd 2022",dateEndFourthStepPublish:"September 20th 2022",dateEndFifthStepPublish:"November 19th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"An expert in the developments of smart sensors and intelligent nanodevices with a wide range of applications, he has received over twenty-eight awards such as TRF–OHEC–SCOPUS Young Researcher Award in physical science, and is listed in the Top 2% World Ranking of Scientists in Electrical & Electronic Engineering in 2020 and 2021 ranked by Stanford University researcher team.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"34521",title:"Associate Prof.",name:"Chatchawal",middleName:null,surname:"Wongchoosuk",slug:"chatchawal-wongchoosuk",fullName:"Chatchawal Wongchoosuk",profilePictureURL:"https://mts.intechopen.com/storage/users/34521/images/system/34521.jpeg",biography:"Chatchawal Wongchoosuk received the Ph.D. and MSc degrees from Mahidol University and the BSc degree with first-class honors in Physics from Prince of Songkla University, Thailand in 2011, 2007, and 2005, respectively. 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1. Introduction
Speech communication can be impaired by the wide range of noise conditions present in air. Researchers in the field of speech applications have been investigating how to improve the performances of signal extraction and its recognition in the conditions. However, it is not yet possible to measure clear speech in environments where there are low Signal-to-Noise Ratios (SNR) of about 0 dB or less (H. Hirsch and D. Pearce, 2000). Standard rate scales, such as CENSREC(N. Kitaoka et al., 2006) and AURORA (H. Hirsch and D. Pearce,2000), are typically discussed for evaluating performances of speech recognition in noisy environments and have shown thatspeech recognition rates are approximately 50–80% whenunder the influence of noise, demonstrating the difficulty ofachieving high percentages.With these backgrounds, many signal extraction and retrieval methods have been proposed in previous research. There is one of approaches in signal extractions, body-conducted speech (BCS) which is little influence from noise in air however it does not measure 2 kHz above in frequency characteristics. However, these need normal speech or parameters measured simultaneously with body-conducted speech. Because these parameters are not measured in noisy environments, the authors have been investigating the use of body-conducted speech which is generally called bone-conducted speech, where the signal is also conducted through the skin and bone in a human body (S. Ishimitsu, 2008) (M. Nakayama et al., 2011). Conventional retrieval methods for sound quality of body-conducted speech are the Modulation Transfer Function (MTF), Linear Predictive Coefficients (LPC), direct filtering and the use of a throat microphone (T. Tamiya, and T. Shimamura, 2006) (T. T. Vu et al., 2006) (Z. Liu et al., 2004) (S. Dupont, et al., 2004). As a research in state-of-the art, the research fields is expanded to speech communicationsbetween a patient and an operator in a Magnetic Resonance Imaging (MRI) room which has a noisy sound environment with a strong magnetic field (A. Moelker et al., 2005). Conventional microphonesuch as an accelerometercomposed of magnetic materials are not allowed in this environment, which requires a special microphone made of non-magnetic material.
For this environment the authors proposed a speech communication system that uses a BCS microphone with an optical fiber bragg grating (OFBG microphone) (M. Nakayama et al., 2011). It is composed of only non-magnetic materials, is suitable for the environment and should provide clear signals using our retrieval method. Previous research using an OFBG microphone demonstrated the effectiveness and performance of signal extraction in an MRI room. Its performance of speech recognition was evaluated using an acoustic model constructed with unspecified normal speech (M. Nakayama et al., 2011). It is concluded that an OFBG microphone can produce a clear signal with an improved performance compared to an acoustic model made by unspecified speeches. The original signal of an OFBG microphone enabled conversation however some stress was felt because its signal was low in sound quality. Therefore one of the research aims is to improve the quality with our retrieval method which used differential acceleration and noise reduction methods.
In this chapter, it will be shown in experiments and discussionsfor the body-conducted speeches with the method which is measured with an accelerometer and an OFBG microphone, as one of topics is a state-of-the-art in the research field of signal extraction under noisy environment. Especially, it is mainly investigated in evaluations of the microphones, signal retrievals with the method and applying the method toa signal in sentence unit long for estimating and recovering of sound qualities.
Speech as air-conducted sound is easily affected by surrounding noise. In contrast, body-conducted speech is solid-propagated sound and thus less affected by noise. A word is uttered by a 20-year-old male in a quiet room. Table 1 details the recording environments for microphone and acclerometer emploied in this research. Speech is measured 30 cm from the mouth using a microphone, and body-conducted speech is extracted from the upper lip using the accelerometer as conventional microphone which is shown in Figure 1. This microphone position is that commonly used for the speech input of a car navigation system. The upper lip, as a signal-extraction position, provides the best cepstral coefficients as feature parameters for speech recognition (S. Ishimitsu et al., 2004). Figures 2 and 3 showuttered words “Asahi” in quiet room, taken from the JEIDA database, which contains 100 local place names(S. Itahashi, 1991). Speech is measured a cleary signal in frequency characteristics however body-conducted speech lacks high-frequency components above 2 kHz.So the performance is reduced when the signal is used for the recognition directory.
Recorder
TEAC RD-200T
Microphone
Ono Sokki MI-1431
Microphone amplifier
Ono Sokki SR-2200
Microphone position
30cm (Between mouth and microphone)
Accelerometer
Ono Sokki NP-2110
Accelerometer amplifier
Ono Sokki PS-602
Accelerometer position
Upper lip
Table 1.
Recording environments for microphone and accelerometer
Figure 1.
Accelerometer
Figure 2.
Speech from microphone in quiet
Figure 3.
BCSfrom accelerometer in quiet
2.2. Optical Fiber Bragg Grating microphone
To extend testing to scenarios such as that in which noise sound is generated with strong magnetic field, in communications between a patient and an operator in an MRI room, an OFBG microphone is employedto record body-conducted speech there because it can measure a clearer signal than an accelerometer and be used in an environment with a strong magnetic field. It is examined the effectiveness of the microphone in an MRI room in which a magnetic field is produced by an open-type magnetic resonance imaging system. Tables 2 and 3 detail the recording environments for OFBG microphone which is shown in Figure 4. Noise levels in the room did not measure at the recording point such as the mouth of the speaker because a sound-level meter did not permit into the room since it composed from magnetic materials. Therefore, the noise level is measured at the entrance of the room, and consequently may be higher than the noise level at the signal recording point; the noise level is given in Table 2. Owing to patient discomfort during the recordings, only 20 words and 5 sentences were recorded in the room where a scene is shown in Figure 5. Figure 6 shows the body-conducted speech recorded from the OFBG microphone in the room when activated a MRI. Compared the signal with conventional BCS, it is clearer than that for body-conducted speech measured by accelerometer because characteristics of frequencies above 2 kHz can be found.
Figure 4.
OFBG microphone
Figure 5.
Signal recording in an MRI room
Figure 6.
BCS from OFBG microphone
MRI model
HITACH AIRIS II
Environment
MRI (OFF): 61.6 dB SPL
MRI (ON): 81.1 dB SPL
Speakers
two males(22 and 23 years old)
two females(23 and 24 years old)
Vocabulary
twenty words × two sets:JEIDA 100 local place names
five sentences × three sets:ATR database sentences
Table 2.
Recording environment 1 for OFBG microphone
Device name
Type name
Pickup
Optoacoustics Optimic4130
Optical-electronic conversion device
Optoacoustics EOU200
Recorder
TEAC LX-10
Table 3.
Recording environment 2 for OFBG microphone
3. Speech recognition with OFBG microphone
The quality of the signal recorded with the OFBG microphone, is higher than the quality of BCS recorded with accelerometer. Generally, the quality of speech sound is evaluated by the mean opinion score from 1 to 5 however this requires much evaluation data to achieve adequate significance levels. For the reason, it is evaluated the sound quality through speech recognition using acoustic models estimated with the speech of unspecified speakers as results of recognition performances. In speech recognition, the best candidate is chosen and decided by likelihoods derived from acoustic models and feature parameters such as cepstral parameters, which are calculated from the recorded speech (D. Li, and D. O’Shaughnessy, 2003) (L. Rabiner, 1993). As a result, the recognition performances and likelihoods are statistical results since human errors and other factors are not considered.
3.1. Experimental conditions
Table 4 shows the experimental conditions for isolated word recognition in speech recognition. The experiment employs the Julius, speech recognition decoder, which is a large-vocabulary continuous-speech recognition system for Japanese language (T. Kawahara et al., 1999) (A. Lee et al., 2001). The decoder requires a dictionary, acoustic models and language models. The dictionary describes connections of sub-words in each word, such as phonemes and syllables, which are the acoustic models. Language models give the probability for a present word given a former wordin corpora. The purpose of the experiment is only the evaluation of the clarity or the similarity of signals and acoustic models. Since language models are not required in this experiment, Julian version 3.4.2 is used for isolated-word recognition especially. Thus, the experimentsare used the same acoustic models estimated by HTK with JNAS to evaluate closeness of signals when highest recognition performance is achieved (S. Young et al., 2000) (K. Itou et al, 1999).
3.2. Experimental results
Table 5 shows recognition results of isolated word recognition in each data set, and Table 6 gives averages of recognition results in each speaker. The recognition results for the OFGB microphone are found to be superior to the recognition results for the conventional BCS microphone. The differences in isolated-word recognition rates are about 15% to 35% respectively. These results show the effectiveness of the OFBG microphone when is measured clearly signals with it.
Speaker
two males (22 and 23 years old) two female (23 and 24 years old)
Number of datasets
20 words × three sets/person
Vocabulary
JEIDA 100 local place names
Recognition system
Julian 3.4.2
Acoustic model
gender-dependent triphone model
Model conditions
16 mixture Gaussian, clustered 3000 states
Feature vectors
MFCC(12)+ΔMFCC(12)+ΔPow(1)=25 dim.
Training condition
more than 20,000 samples JANS with HTK 2.0
Table 4.
Experimental conditions for isolated word recognition
Speaker
MRI off
MRI on
set 1
set 2
set 3
set 1
set 2
set 3
Male 1
85%
80%
90%
30%
40%
50%
Male 2
90%
75%
85%
50%
60%
60%
Female 1
35%
35%
35%
20%
20%
20%
Female 2
80%
70%
70%
75%
70%
75%
Table 5.
Recognition results of isolated word recognition in each data set
Speaker
MRI off
MRI on
Male 1
85.0%
40.0%
Male 2
83.3%
56.7%
Female 1
35.0%
20.0%
Female 2
73.3%
73.3%
Table 6.
Averagesof recognition results
4. Improvement on sound quality of body-conducted speech in word unit
The OFBG microphone can measure a high quality signal compared to a BCS of an accelerometer. To realize conversations without stress, signals with improved in sound qualitiesare required. Consequently, one of aims in the research is to invent and examine a method for improving sound quality.Many researchers and researches which are already introduced in the chapter of introduction, are unaware that a BCS does not have frequency components 2 kHz and higher. Mindful of this condition, conventional retrieval methods for BCS that need the speech and its parameters are proposed and investigated, however speech is not measured easily in noisy environments. Therefore a signal retrieval method for a BCS only performs well with itself. In realizing this progressive idea, the method is invented a signal retrieval method without speech and the other parameters becauseeffective frequency components in signals over 2 kHz are found however there contains very low gains.
4.1. Differential acceleration
Formula (1) shows an equation for estimating using the differential acceleration from the original BCS.
xdifferential(i)=x(i+1)−x(i)E1
xdifferential(i) is the differential acceleration signal that is calculated from each frame of a BCS. Because of low gains in its amplitude, it requires adjusting to a suitable level for hearing or processing. Figure 7 shows a differential acceleration estimated from Figure 6 using Formula (1), with the adjusted gain. It seems that the differential acceleration signal is composed of speech mixed with stationary noise, so we expected to be able to remove it completely with the noise reduction method because the signal has a high SNR compared to the original signal. Consequently, it isproposed the signal estimation method using differential acceleration and a conventional noise reduction method (M. Nakayama et al., 2011).
4.2. Noise reduction method
As a first approach to noise reduction, it is examined the effectiveness of a spectral subtraction method for the reduction of stationary noise. However, improvements in performancesfor the frequency components is inadequated with this approach. The noise spectrum is simply subtracted by a spectral subtraction method, so a Wiener-filtering method is expected to estimate the spectrum envelope of speech using linear prediction coefficients. Therefore, it is tried to extract a clear signal using the Wiener-filtering method, which could estimate and obtain the effective frequency components from noisy speech. Formula (2) shows the equation used for the Wiener-filtering method.
HEstimate(ω)=HSpeech(ω)HSpeech(ω)+HNoise(ω)E2
An estimated spectrum HEstimate(ω) can be converted to a retrieval signal from the differential acceleration signal. It can be calculated from the speech spectrum HSpeech(ω) and noise spectrum HNoise(ω). In particular, HSpeech(ω) is calculated with autocorrelation functions and linear prediction coefficients using a Levinson-Durbin algorithm (J. Durbin, 1960), and HNoise(ω) is then estimated using autocorrelation functions.
4.3. Evaluations
Signal retrieval for a signal measured by an OFBG microphone is performed using the same parameters in the method because a propagation path of body-conducted speech in a human body is not affected by either quiet or noisy environments. Figure 8 shows a retrieval signal from Figure 7 using a Wiener-filtering method where the linear prediction coefficients and autocorrelation functions are 1 and the frame width is 764 samples. These procedures were repeated five times on a signal to remove a stationary noise. From a retrieval signal, high frequency components from 2 kHz and above were recovered with these settings. This proposed method could also be applied to obtain a clear signal from body-conducted speech measured with OFBG microphone in noisy sound and high magnetic field environment.
Figure 7.
Differential acceleration from OFBG microphone
Figure 8.
Retrieval signal from OFBG microphone
5. Improvement on sound quality of body-conducted speech in sentence unit
The effectiveness of signal retrieval for body-conducted speech in word unit measured by an accelerometer and an OFBG microphone has been demonstrated at former sections. Howeverthe effectiveness of body-conducted speech in word unit is proven,signals in sentence unit need to be examined for practical use such as conversations in the noisy environment. Though the investigation for the sentence unit is an important evaluation, so it could revolutionize speech communications in the environment. As a first step in signal retrieval for sentence unit, themethod adoptsthe method to signals in word unit because the transfer function between the microphone and sound source seems to change little whether word or sentence unit, and is examineda body-conducted speech insentence unit directly measured by an accelerometer and an OFBG microphone.
5.1. Body-conducted speech from an accelerometer
In experiments on signal retrieval using an accelerometer, speech and body-conducted speech were measured in a quiet room of our laboratoryand engine room of the training ship at the Oshima National College of Maritime Technology, where there is noisy environments with working a main engine and two generator, are shown Figures 9 (a) and (b). The recording environment is also used Table 1, however the speaker who uttered a word differs from a speaker in a former section. Noise within the engine room, under the two conditions of anchorage and cruising, were 93 and 98 dB SPL, respectively, and the SNR measurements from microphone. There was –20 and –25 dB SNR, respectively. In this research, the signal is experimented under cruisingcondition to estimate retrieval signals.
A 22-year-old male uttered A01 sentencefrom the ATR503 sentence database, andthe sentence is a commonly used sentence in speech recognition and application (M. Abe et al., 1991). And the sentence is composed of the followings in sub-word of mora.
Figures10 and 11 show a speech and a body-conducted speech insentence unit measured by a conventional microphone and accelerometerin a quiet room when a 22 years-old male uttered the sentence. Although the accelerometeris held with fingers, soundsare measured clearly because it was firmly held to the upper lip with a suitablepressure. Figure 12 shows a differential acceleration from Figure 11, becomes clearly signal with little noise because the BCS is high SNR.
Figures 13 and 14 show a speech and a body-conducted speech in sentence unit in the noisy environment. Speech is completely swamped by the intense noise from the engine and generators. On the other hand, body-conducted speech in Figure 14 is affected a little by the noise but can be measured. Because SNR in Figure 14 has low gain, differentialacceleration in Figure 15 is considered that the performance of signal retrieval is reduced. Figure 16 shows the signal retrieval from the differential accelerationworks well when the treated four times since the performance is sufficient to recover the frequency characteristics. As a result, it is concluded that body-conducted speech is as clear as possible without noise disturbance.
Figure 10.
Speech of sentence in quiet
Figure 11.
BCS of sentence in quiet
Figure 12.
Differential accelerationof sentence in quiet
Figure 13.
Speech of sentence innoise environment
Figure 14.
BCS of sentence in noise environment
Figure 15.
Differential acceleration of sentence in noise environment
Figure 16.
Retrieval BCS of sentence in noise environment
5.2. Body-conducted speech from OFBG microphone
The quality of the signal measured by the OFBG microphone in the noisy environment of an MRI room was investigated here. A speaker uttered the sentence A01 during the operation of MRI devices, such that there was an 81 dB SPL-noise environment. Although a sound level meter was not permitted in the room, so itis measured in front of the gate door in the room.Figure 17 shows the signal of the uttered sentence recorded by the OFBG microphone in the MRI room when MRI equipment was in operation. Since the signal is clear, it is expected that the frequency characteristics of the signal can be recovered employing the signal retrieval method. Figures 18 and 19 show the differential acceleration and retrieved signal from the OFBG microphone in the MRI room when the MRI equipment was in operation and the method treated three times. These figures confirm to improve in the sound quality of BCS in sentence, and it also concluded that the SNR in BCS is best when it has high level.
Figure 17.
BCS of sentence in MRI room
Figure 18.
Differential acceleration of sentence in MRI room
Figure 19.
Retrieval signal of sentence in MRI room
6. Conclusions and future works
This section presentsimprovements on sound quality of body-conducted speeches measured with an accelerometer and an OFBG microphone. Especially, an MRI room has heavy noisy sound and high magnetic field environment. The environment does not allow bringingaccelerometer such as a conventional body-conducted speech microphone which is made from magnetic materials. For conversations and communications between a patient and an operator in the room, an OFBG microphone is proposed, which can measure clear signals compared to accelerometer.
And then, the performances of signal retrieval method in sentence with the microphones that are an accelerometer and an OFBG microphone were evaluated, and the effectiveness is confirmed with time–frequency analysis and speech recognition. From this background, it is investigated estimating clear body-conducted speech in sentence unit from an OFBG microphone with our signal retrieval method that used combined differential acceleration and noise reduction. Applying the method to the signal measured recovered which in sound quality that was evaluated using time-frequency analysis. Thus, its retrieval method can also be applied to a signal measured by an OFBG microphone with the same settings because its conduction path is not affected by the noise in the air. The signals were measured in quiet and noisy rooms, specifically an engine room and MRI room. The signals were clearly obtained employing the signal retrieval method and the same settings used for the word unit as a first step. To obtain a clearer signal with the signal retrieval method, the pressure at which the microphone is held is important, and the sounds have high SNR in original BCS.
As future works, it needs to extend the signal retrieval method for practical use and improvement of algorithm for advance.
Acknowledgement
The authors thank Mr. K. Oda, Mr. H. Nagoshi and his colleagues in Ishimitsu laboratory of Hiroshima City University, members of the Living Informatics Research Group, Health Research Institute, National Institute of Advanced Industrial Science and Technology (AIST) for their support in the signal recording, and crew members of the training ship, Oshima-maru, Oshima National College of Maritime Technology.
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Introduction",level:"1"},{id:"sec_2",title:"2. Speech and body-conducted speech",level:"1"},{id:"sec_2_2",title:"2.1. Conventional body-conducted speech microphone",level:"2"},{id:"sec_3_2",title:"2.2. Optical Fiber Bragg Grating microphone",level:"2"},{id:"sec_5",title:"3. Speech recognition with OFBG microphone",level:"1"},{id:"sec_5_2",title:"3.1. Experimental conditions",level:"2"},{id:"sec_6_2",title:"3.2. Experimental results",level:"2"},{id:"sec_8",title:"4. Improvement on sound quality of body-conducted speech in word unit",level:"1"},{id:"sec_8_2",title:"4.1. Differential acceleration",level:"2"},{id:"sec_9_2",title:"4.2. Noise reduction method",level:"2"},{id:"sec_10_2",title:"4.3. Evaluations",level:"2"},{id:"sec_12",title:"5. Improvement on sound quality of body-conducted speech in sentence unit",level:"1"},{id:"sec_12_2",title:"5.1. Body-conducted speech from an accelerometer",level:"2"},{id:"sec_13_2",title:"5.2. Body-conducted speech from OFBG microphone",level:"2"},{id:"sec_15",title:"6. Conclusions and future works",level:"1"},{id:"sec_16",title:"Acknowledgement",level:"1"}],chapterReferences:[{id:"B1",body:'LeeA.KawaharaT.ShikanoK.2001Julius- an open source real-time largevocabulary recognition engine, in Proceedings of European Conference on SpeechCommunication and Technology (EUROSPEECH), 16911694\n\t\t\t'},{id:"B2",body:'MoelkerA.MaasR. A. J. J.VogelM. W.OuhlousM.PattynamaP. M. T.2005Importance of bone-conducted sound transmission on patient hearing in the MR scanner, Journal of Magnetic Resonance Imaging, 221163169\n\t\t\t'},{id:"B3",body:'LiD.D.O’Shaughnessy(2003Speech Processing: ADynamic and Optimization-Oriented Approach, MarcelDekker Inc.'},{id:"B4",body:'HirschH.PearceD.2000The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions, in proceedings of ISCA ITRW ASR2000, 181188\n\t\t\t'},{id:"B5",body:'DurbinJ.1960The Fitting of Time-Series Models, Review of the International Statistical Institute, 283233244\n\t\t\t'},{id:"B6",body:'ItouK.YamamotoM.TakedaK.TakezawaT.MatsuokaT.KobayashiT.ShikanoK.ItahashiS.1999JNAS : Japanese speech corpus for large vocabulary continuous speech recognition research, Journal of the Acoustical Society of Japan (E), 20(3), 199206\n\t\t\t'},{id:"B7",body:'L.Rabiner(1993Fundamentals of Speech Recognition, PrenticeHall'},{id:"B8",body:'AbeM.SagisakaY.UmedaT.KuwabaraH.1990Manual of Japanese Speech Database, ATR'},{id:"B9",body:'NakayamaM.IshimitsuS.NakagawaS.2011A study of making clear body-conducted speech using differential acceleration, IEEJ Transactions on Electrical and Electronic Engineering, 62144150\n\t\t\t'},{id:"B10",body:'NakayamaM.IshimitsuS.NagoshiH.NakagawaS.FukuiK.2011Body-conducted speech microphone using an Optical Fiber Bragg Grating for high magnetic field and noisy environments, in proceedings of Forum Acusticum 2011'},{id:"B11",body:'KitaokaN.YamadaT.TsugeS.MiyajimaC.NishiuraT.NakayamaM.DendaY.FujimotoM.YamamotoK.TakiguchiT.KuroiwaS.TakedaK.S.Nakamura(2006CENSREC-1-C: development of evaluationframework for voice activity detection under noisyenvironment, IPSJ SIG Technical Report, 2006-SLP-63,16\n\t\t\t'},{id:"B12",body:'DupontS.RisC.BachelartD.2004Combined use of closetalk and throat microphones for improved speech recognition under non-stationary background noise, in proceedings of COST278 and ISCA Tutorial and Research Workshop (ITRW) on Robustness Issues in Conversational Interaction, paper31'},{id:"B13",body:'IshimitsuS.2008Construction of a Noise-Robust Body-Conducted Speech Recognition System, in Chapter of Speech Recognition, IN-TECH'},{id:"B14",body:'IshimitsuS.KitakazeH.TsuchibushiY.YanagawaH.FukushimaM.2004A noise-robust speech recognition system making use of body-conducted signals, Acoustical Science and Technology, 252166169\n\t\t\t'},{id:"B15",body:'IshimitsuS.NakayamaM.MurakamiY.2004Study of Body-Conducted SpeechRecognition for Support of Maritime Engine Operation, in Journal of the JIME,3943540in Japanese)'},{id:"B16",body:'ItahashiS.1991A noise database and Japanese common speech data corpus, Journal of ASJ, 4712951953\n\t\t\t'},{id:"B17",body:'YoungS.JansenJ.OdellJ.WoodlandP.2000The HTK Book for 2CambridgeUniversity'},{id:"B18",body:'KawaharaT.LeeA.KobayashiT.TakedaK.MinematsuN.ItouK.ItoA.YamamotoM.YamadaA.UtsuroT.ShikanoK.1999Japanese dictation toolkit- 1997version, Journal of ASJ, 203233239\n\t\t\t'},{id:"B19",body:'VuT. T.UnokiM.AkagiM.2006A Study on Restoration of Boneconducted Speech With LPC Based Model, IEICE TechnicalReport, SP2005-174, 6778\n\t\t\t'},{id:"B20",body:'TamiyaT.ShimamuraT.2006Improvement of Body-Conducted Speech Quality by Adaptive Filters, IEICE Technical Report, SP2006-191, 4146\n\t\t\t'},{id:"B21",body:'LiuZ.ZhangZ.AceroA.DroppoJ.HuangX.2004Direct Filtering for Air- and Bone-Conductive Microphones, in proceedings of IEEE International Workshop on Multimedia Signal Processing (MMSP’04), 363366\n\t\t\t'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Masashi Nakayama",address:null,affiliation:'
Kagawa National College of Technology, Japan
National Institute of Advanced Industrial Science and Technology (AIST), Japan
National Institute of Advanced Industrial Science and Technology (AIST), Japan
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\n
1. Introduction
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Medical imaging is the process of producing visible images of inner structures of the body for scientific and medicinal study and treatment as well as a visible view of the function of interior tissues. This process pursues the disorder identification and management. This process creates data bank of regular structure and function of the organs to make it easy to recognize the anomalies. This process includes both organic and radiological imaging which used electromagnetic energies (X-rays and gamma), sonography, magnetic, scopes, and thermal and isotope imaging. There are many other technologies used to record information about the location and function of the body. Those techniques have many limitations compared to those modulates which produce images. Annually billions of images have been done globally for different diagnostic purposes. About half of them use ionizing and nonionizing radiation modulates [1]. Medical imaging produces the images of the internal structures of the body without invasive procedures. Those images were produced using fast processors and due to conversion of the energies arithmetically and logically to signals [2]. Those signals later are converted to digital images. Those signals represent the different types of tissues inside the body.
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The digital images play a necessary role on a daily basis. The medical imaging processing refers to handling images by using the computer. This processing includes many types of techniques and operations such as image gaining, storage, presentation, and communication. The image is a function that signifies a measure of characteristics such as illumination or color a viewed sight. The digital images have several benefits such as faster and cheap processing cost, easy storing and communication, immediate quality assessment, multiple copying with reserving the quality, fast and cheap reproduction, and adaptable manipulation. The disadvantages of digital images are exploitation copyright, inability to resize with preserving the quality, the need of large-capacity memory, and the need of faster processor for manipulation [3].
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An image processing technique is the usage of computer to manipulate the digital image. This technique has many benefits such as elasticity, adaptability, data storing, and communication. With the growth of different image resizing techniques, the images can be kept efficiently. This technique has many sets of rules to perform into the images synchronously. The 2D and 3D images can be processed in multiple dimensions. The image processing techniques were founded in the 1960s. Those techniques were used for different fields such as Space, clinical purposes, arts, and TV image improvement. In the 1970s with the development of computer system, the cost of image processing became less and faster. In the 2000s, the image processing became quicker, inexpensive, and simpler [4].
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The human visual system is one of the most complex schemes that ever existed. This system allows living beings to organize and understand the many complex elements in their external environment. The visual system comprises of the eye that transmutes light into neural signals and the related parts of the brain that process those signals and excerpt essential data. The human eye is bilateral cylinder structures that are located anteriorly in the skull. The eyes are 2.5 cm in both crosswise and lengthwise diameters. In the middle of the eyeball, there is a blackened structure called the pupil. This system permits the light to cross the eye. This system narrows when exposed to a heavier light source. This reduces the light to the retina and enhances the visual process. There are many muscles surrounding the eye and that control the widening of the pupil. The eye always has some supporting structures called the sclera. The lens is a ligamentous part located behind the cornea. The shape of the lens changes continuously due to muscle contraction [4, 5]. Figure 1 shows the cross-sectional view of the eyeball.
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Figure 1.
The eyeball.
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The light concentrates into the middle part of the eye and focuses from the cornea and lens on retinae. The fovea emphases the image into the retina. Finally, the brain forms the details and colors using its perception through multiple processes.
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2. Classification of digital images
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The digital images have two main types of images. Raster image is described as a four-sided arrangement of frequently sampled values known as pixels. The digital images are usually inaccessible images and involve multifaceted color difference. The digital images have fixed resolution due to their pixels size. The digital images lose their quality in the resizing process due to some missing data. The digital images are used mainly in photography images because of their good color shades. The image-gaining instrument controls the resolution. The digital images include many formats such as BMP (Windows bitmap), TIFF (Tag Interleave Format), PCX (Paintbrush), PNG (Portable Network Graphics), etc. [6, 7].
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A vector is described as a wrinkled and a bent object that is defined precisely by the computer. The vector has many qualities such as line width, dimension, and hue. The vectors are easily scalable images and can be reproduced in different magnitudes without change in its quality. The vectors are suitable for design, line painting, and diagrams.
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3. Applications of digital image processing
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The digital image processing has many applications in the medical field such as:
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3.1 Medicine
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In medicine, many techniques are used such as segmentation and texture analysis, which is used for cancer and other disorder identifications. Image registering and fusion methods are widely used nowadays specially in new modalities such as PET-CT and PET-MRI. In the field of bioinformatics, telemedicine and the formatless compression techniques are used to communicate the image remotely [1, 2, 3, 4, 5].
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3.2 Forensics
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The common techniques used in this field are edge detection, pattern matching, denoising, security, and biometric purposes such as identity, face, and fingerprint documentation. Forensics is based on the database information about the individuals. Forensics matches the input data (fingerprint, eye, photo, etc.) with the database to define the person identity [2].
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4. Medical imaging systems
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Medical imaging systems use the signals received from the patient to produce images. Medical imaging systems use both ionizing and nonionizing sources.
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4.1 X-ray imaging systems
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Since the discovery of X-rays by the German scientist Roentgen, X-rays have been used to image the body parts for diagnostic purposes. In X-ray tube, the electrons are produced in cathode through a thermal emission process and are accelerated through a potential difference of 50–150 KV. The electrons hit the anode to produce the X-rays. Only 1% of this energy is converted to X-rays, and the remaining amount is changed to heat (Figure 2) [3].
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Figure 2.
X-rays tube.
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In the X-ray machines, the images are produced in 2D plans of the examined part of the body. The fluoroscopy system is used to scan the moving organs. The acquired images can be displayed, stored, and communicated through different machines. Computed radiography (CT) uses image receptor to produce the image. X-rays accompany a screen covered with a storage phosphor device. The mammography imaging is used to differentiate between the breast tissues and different diseases. Mammography imaging uses lower energy compared with bony structure imaging. The range of potential difference used is 15–40 kV (Figure 3) [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16].
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Figure 3.
Mammography image.
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4.2 Computed tomography (CT)
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In this modality, the images are produced in multiple dimensions rather than the conventional radiography. CT scanner produces multiple slices of the body tissues in different directions. In CT scanner, the patient is placed inside its aperture and scanned by a rotating X-ray tube in all directions (Figure 4) [6].
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Figure 4.
CT scanner.
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4.3 Nuclear medicine
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This imaging modality uses the radioisotopes to produce images about the functions of the different structures such as the heart, kidney, and liver. The radioisotopes are labeled by pharmaceutical materials to be guided to the certain organs. The patient’s emitted photons are received in the detectors and convert into signals. Those signals are converted to interpretable digital images. There are many types of nuclear medicine scanning modalities such as planar, tomographic, and positron emissions. The planar emission produces 2D images. Both of the tomographic and the positron emissions produce 3D images (Figure 5) [5].
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Figure 5.
Nuclear medicine imaging.
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4.4 Ultrasound
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Ultrasound is a technique, which uses high-frequency sound waves to produce images of the internal structure of the body from the returned echoes. Ultrasound is similar to the location determination technique, which is used by some animals like bats and whales in the nature. Ultrasound is transmitted in high-frequency pulses into the body using a transducer as those waves travel through the body tissues. Some of those waves are absorbed and some reflect back. The reflected waves are received by the transducer and converted into electric signals. Those electric signals are converted into digital ones and pass through the computer system. The computer system uses the arithmetic and logic calculation to form the 2D image of the scanned structures. In the ultrasonic system, thousands of pulses are sent per each millisecond. There are many imaging techniques used to enhance the ultrasound images (Figure 6) [1, 2, 3, 4, 5, 6].
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Figure 6.
Ultrasound imaging diagram.
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5. Fundamentals of digital image processing
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The images are classified according to different qualities such as illumination, contrast, entropy, and signal-to-noise ratio. The histogram is the simplest image processing technique. The image display does not change the image quality. The grayscale histogram considers the basic type of the images that are used to evaluate and to improve the images. The histogram is a scheme showing the pixels’ values and not their locations. The gray-level histogram shows whether an image is generally shady or bright (Figure 5). The mean pixel value is obtained from the histogram by summing the produced pixel values and consistent bin altitude and dividing by the entire number of pixels [7, 8]. Histogram equalization is used to compare many images acquired on definite bases. The technique works by changing the histogram to become smooth, identical, and balanced (Figure 7).
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Figure 7.
Sagittal MRI image of a head had enhanced using (i) image adjustment, (ii) histogram equalization, and (iii) adaptive histogram equalization [8].
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The mean value of central pixel intensity is designated to the ideal brightness. Any intensity above or below makes the image darker or brighter. Signal-to-noise ratio (SNR) of an image is used to associate the level of the anticipated signal to the level of the contextual signal. Signal-to-noise ratio (SNR) is defined as the ratio of signal intensity to noise intensity. Signal-to-noise ratio (SNR) calculates from the image in an up-front method. The mean intensity of the image is expressed as the square of the mean of the pixel value (Eq. (1)).
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\n\nSNR\n=\n\n\nP\nsignal\n\n\nP\nnoise\n\n\n\nE1
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where p is the average power.
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5.1 Image enhancement
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Image enhancement is a technique used to improve the image quality and perceptibility by using computer-aided software. This technique includes both objective and subjective enhancements. This technique includes points and local operations. The local operations depend on the district input pixel values. Image enhancement has two types: spatial and transform domain techniques. The spatial techniques work directly on the pixel level, while the transform technique works on Fourier and later on the spatial technique (see Figures 8 and 9) [9].
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Figure 8.
Edge-aware local contrast manipulation of thyroid scan images (a), (b) edge threshold, (c) original image and (d) reduced contrast −0.5 [12].
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Figure 9.
Edge-aware local contrast manipulation of leukemia cell images (a) and (c) original image, (b) Edge threshold, and (d) Reduced contrast −0.5 [13].
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5.2 Image segmentation
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Image segmentation is a technique of segregating the image into many parts. The basic aim of this segregation is to make the images easy to analyze and interpret with preserving the quality. This technique is also used to trace the objects’ borders within the images. This technique labels the pixels according to their intensity and characteristics. Those parts represent the entire original image and acquire its characteristics such as intensity and similarity. The image segmentation technique is used to create 3D contour of the body for clinical purposes. Segmentation is used in machine perception, malignant disease analysis, tissue volumes, anatomical and functional analyses, 3D-rendered technique, virtual reality visualization and anomaly analysis, and object definition and detection (Figure 10) [12, 13, 14].
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Figure 10.
Segmentation process of (a) thyroid gland and heart [3].
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Image segmentation is divided into kinds: (i) local segmentation and (ii) global segmentation. The local segmentation works particularly in one subdivision of the image. This technique has a fewer number of pixels compared to the global type. The global segmentation works in the whole image as one unit. This technique has more pixels to manipulate. Segmentation can be divided into methods:
Thresholding segmentation depends on the threshold value to convert the gray color-based image into black and white [4]. There are many other techniques applied in radiology in order to rebuild or reslice the images such as Otsu’s and k-means techniques [5, 6]. Threshold method is useful for establishing the borders of solid objects in a dark background. Threshold techniques need presence of differences between the object’s and background’s intensities. There are three types of thresholding methods. Those methods include global, adaptive, and histogram-built selection threshold. The global threshold is broader and used for all segmentation techniques. The global threshold (θ) calculates using binarization procedure as in the following equation (Eq. (2)):
The adaptive or fixed threshold segments image faster if the region of interest contains unique intensity and is different from the background. The disadvantage of this method is its simplicity and inability to process the multichannel images [15].
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\n
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5.4 Image segmentation based on edge detection
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Edge detection is a segmentation technique that uses border recognition of strictly linked objects or regions. This technique identifies the discontinuity of the objects. This technique is used mainly in image study and to recognize the parts of image where a huge variation in intensity arises.
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5.5 Some types of edge detection
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5.5.1 Roberts kernel
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Roberts kernel is a technique used for determining the difference between two close pixels. Precisely it is called forward differences. This technique can find the edges in high noised images; it is calculated using first-order fractional derivative and cross-gradient operator (Eqs. (3) and (4)) (Figure 11) [21].
This technique is based on the idea of central difference. This technique is better than Roberts operator (Figure 11). Assume that matric has arrangement of pixels [i, j] as in Eq. (6):
where c is constant and expresses the pixels closed to the center of the image. \n\n\nG\nx\n\n\n and \n\n\nG\ny\n\n\n are the calculations at [i, j]. When c equals 1, the Prewitt operator is calculated as in Figure 10 and Eq. (8) [15, 16]:
This technique can be dependent on the central difference which tends toward the central pixels in average. This technique can be expressed as 3 × 3 matric to the first derivative of Gaussian kernel. This technique is calculated as shown in Eqs. (9)–(12) [20, 21, 22]:
The Sobel is better than Prewitt in noise reduction [18]. This technique is used in the functional imaging modality such as nuclear medicine. In the study of red blood cell images, the unraveling of strictly neighboring cells is considered difficult issues due to the background noise. This affects the interpreting processes and makes them difficult to diagnose by the physician. Segmentation can solve such problems and identify those red cells easily (see Figure 12) [17].
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Figure 12.
Red blood cell segmentation using edge detection: (a) original image and (b) Sobel and (c) Prewitt techniques.
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\n
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5.6 k-means segmentation
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k-means cluster is a technique of vector and signal valuations. This technique subdivides the image into n parts and into k clusters in which each observation fits to a cluster with a similar mean. k-means clustering tends to find clusters of comparable spatial extent. Given a set of comments (x1, x2, …, xn), where each comment is a d-dimensional actual vector, k-means clustering aims to subdivide the n observations into k (≤ n) sets S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of squares (Eqs. (12) and (13)) [16].
k-means technique can be applied in large databases because of its simplicity. This technique is used in economical, stargazing, cultivation, and computer perception (Figure 13) [17, 18, 19].
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Figure 13.
k-means segmentation technique of nuclear medicine images.
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6. Conclusion
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Images are the method of expression of the data in pictographic form. Images consist of various small elements called pixels. Each pixel has a specific position and value. Geometric image signifies an image arithmetically with geometrical primitives such as lines. Each image is saved in a specific file format, which consists of two parts, the heading and the data. Imaging processing techniques is a group of approaches that are used for handling the images by computer. The objective of segmentation is the partition of the images into important portions. Local segmentation deals with the partition of the images into small parts within the images. Global segmentation deals with the assembly of those partitions. Image segmentation works in three methods, which are region, border, and edge. Region method is used to examine images and region class of neighboring pixels. Thresholding segmentation uses the histogram and threshold value of pixels. Image edge techniques are used to analyze the images at borders or discontinuing. Those techniques include Roberts, Prewitt, Sobel, and Frei-Chen.
\n
\n
Acknowledgments
\n
The authors are thankful to the Deanship of Scientific Research, at Majmaah University, for funding this research.
\n
Conflict of interest
There are no conflicts of interest.
\n',keywords:"medical, imaging, image processing technique",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67331.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67331.xml",downloadPdfUrl:"/chapter/pdf-download/67331",previewPdfUrl:"/chapter/pdf-preview/67331",totalDownloads:4345,totalViews:0,totalCrossrefCites:5,dateSubmitted:"September 12th 2018",dateReviewed:"January 13th 2019",datePrePublished:"June 24th 2019",datePublished:"November 27th 2019",dateFinished:"May 24th 2019",readingETA:"0",abstract:"Medical imaging is the procedure used to attain images of the body parts for medical uses in order to identify or study diseases. There are millions of imaging procedures done every week worldwide. Medical imaging is developing rapidly due to developments in image processing techniques including image recognition, analysis, and enhancement. Image processing increases the percentage and amount of detected tissues. This chapter presents the application of both simple and sophisticated image analysis techniques in the medical imaging field. This chapter also summarizes how to exemplify image interpretation challenges using different image processing algorithms such as k-means, ROI-based segmentation, and watershed techniques.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67331",risUrl:"/chapter/ris/67331",signatures:"Yousif Mohamed Y. Abdallah and Tariq Alqahtani",book:{id:"8125",type:"book",title:"Medical Imaging",subtitle:"Principles and Applications",fullTitle:"Medical Imaging - Principles and Applications",slug:"medical-imaging-principles-and-applications",publishedDate:"November 27th 2019",bookSignature:"Yongxia Zhou",coverURL:"https://cdn.intechopen.com/books/images_new/8125.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78923-872-3",printIsbn:"978-1-78923-871-6",pdfIsbn:"978-1-78985-724-5",isAvailableForWebshopOrdering:!0,editors:[{id:"259308",title:"Dr.",name:"Yongxia",middleName:null,surname:"Zhou",slug:"yongxia-zhou",fullName:"Yongxia Zhou"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"274452",title:"Dr.",name:"Yousif",middleName:"Mohamed",surname:"Abdallah",fullName:"Yousif Abdallah",slug:"yousif-abdallah",email:"y.yousif@mu.edu.sa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",institution:{name:"Majmaah University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"294009",title:"Dr.",name:"Tariq",middleName:null,surname:"Alqahtani",fullName:"Tariq Alqahtani",slug:"tariq-alqahtani",email:"t.alqahtani@mu.edu.sa",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Classification of digital images",level:"1"},{id:"sec_3",title:"3. Applications of digital image processing",level:"1"},{id:"sec_3_2",title:"3.1 Medicine",level:"2"},{id:"sec_4_2",title:"3.2 Forensics",level:"2"},{id:"sec_6",title:"4. Medical imaging systems",level:"1"},{id:"sec_6_2",title:"4.1 X-ray imaging systems",level:"2"},{id:"sec_7_2",title:"4.2 Computed tomography (CT)",level:"2"},{id:"sec_8_2",title:"4.3 Nuclear medicine",level:"2"},{id:"sec_9_2",title:"4.4 Ultrasound",level:"2"},{id:"sec_11",title:"5. Fundamentals of digital image processing",level:"1"},{id:"sec_11_2",title:"5.1 Image enhancement",level:"2"},{id:"sec_12_2",title:"5.2 Image segmentation",level:"2"},{id:"sec_13_2",title:"5.3 Image segmentation based on thresholding",level:"2"},{id:"sec_14_2",title:"5.4 Image segmentation based on edge detection",level:"2"},{id:"sec_15_2",title:"5.5 Some types of edge detection",level:"2"},{id:"sec_15_3",title:"5.5.1 Roberts kernel",level:"3"},{id:"sec_16_3",title:"5.5.2 Prewitt kernel",level:"3"},{id:"sec_17_3",title:"5.5.3 Sobel kernel",level:"3"},{id:"sec_19_2",title:"5.6 k-means segmentation",level:"2"},{id:"sec_21",title:"6. Conclusion",level:"1"},{id:"sec_22",title:"Acknowledgments",level:"1"},{id:"sec_25",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Abdallah Y. Improvement of sonographic appearance using HAT-TOP methods. International Journal of Science and Research (IJSR). 2015;4(2):2425-2430. DOI: http://dx.doi.org/10.14738/jbemi.55.5283\n'},{id:"B2",body:'Abdallah Y. Increasing of edges recognition in cardiac scintigraphy for ischemic patients. Journal of Biomedical Engineering and Medical Imaging. 2016;2(6):40-48. DOI: http://dx.doi.org/10.14738/jbemi.26.1697\n'},{id:"B3",body:'Abdallah Y. Application of Analysis Approach in Noise Estimation, Using Image Processing Program. Germany: Lambert Publishing Press GmbH & Co. KG; 2011. pp. 123-125\n'},{id:"B4",body:'Abdallah Y, Yousef R. Augmentation of X-rays images using pixel intensity values adjustments. International Journal of Science and Research (IJSR). 2015;4(2):2425-2430\n'},{id:"B5",body:'Abdallah Y. Increasing of Edges Recognition in Cardiac Scintography for Ischemic Patients. Germany: Lambert Publishing Press GmbH & Co. KG; 2011. pp. 123-125\n'},{id:"B6",body:'Abdallah YM. History of medical imaging. Archives of Medicine and Health Sciences. 2017;5:275-278. DOI: 10.4103/amhs.amhs_97_17\n'},{id:"B7",body:'Abdallah Y. An Introduction to PACS in Radiology Service: Theory and Practice. Germany: LAP LAMBERT Academic Publishing; 2012. ISBN: 978-3846588987\n'},{id:"B8",body:'Abdallah Y. Application of Analysis Approach in Noise Estimation: Using Image Processing Program. LAP LAMBERT Academic Publishing; 2011. ISBN: 978-3847331544\n'},{id:"B9",body:'Abdallah Y. Computed Verification of Light and Radiation Field Size Superimposition On Cobalt-60 machine, Verification of Fields Size using Image Processing Technique. Germany: LAMBERT Academic Publishing GmbH & Co. KG; 2010. ISBN: 9783838399096\n'},{id:"B10",body:'Abdallah Y, Mohamed E. Improvement of bone scintography image using image texture analysis. Frontiers in Biomedical Sciences. 2016;1(1):1-6\n'},{id:"B11",body:'Abdallah Y. Segmentation of salivary glands in nuclear medicine images using edge detection tools. Journal of Biomedical Engineering and Medical Imaging. 2016;3(2):1-6. DOI: http://dx.doi.org/10.14738/jbemi.32.1702\n'},{id:"B12",body:'Abdallah Y, Mohamed S. Automatic recognition of leukemia cells using texture analysis algorithm. International Journal of Advanced Research (IJAR). 2016;4(1):1242-1248\n'},{id:"B13",body:'Abdallah Y, Algaddal A, Alkhir M. Enrichment of ultrasound images using contrast enhancement techniques. International Journal of Science and Research (IJSR). 2015;4(1):2381-2385\n'},{id:"B14",body:'Abdallah Y. Increasing the precision of edges recognition in static renal scintography. Indian Journal of Applied Research (IJAR). 2015;4(7):270-273\n'},{id:"B15",body:'Abdallah Y, Wagiallah E, Yousef M. Improvement of nuclear cardiology images for ischemic patients using image processing techniques. SMU Medical Journal. 2015;2(2):1-9\n'},{id:"B16",body:'Abdallah Y. Lungs detection in ventilation and perfusion scintigraphy using watershed transform. International Journal of Electronics Communication and Computer Engineering (IJECCE). 2015;2(3):416-419\n'},{id:"B17",body:'Abdallah Y. An accurate liver segmentation method using parallel computing algorithm. Journal of Biomedical Engineering and Medical Imaging. 2015;3(2):15-23\n'},{id:"B18",body:'Abdallah, Abdallah M. Using basic morphology tools in improvement of kidneys detection. International Journal of Science and Research (IJSR). 2015;4(5):1383-1386\n'},{id:"B19",body:'Shapiro LG, Stockman GC. Computer Vision. New Jersey: Prentice-Hall; 2001. pp. 279-325. ISBN: 0-13-030796-3\n'},{id:"B20",body:'Lauren B, Lee LW. Perceptual information processing system. Paravue Inc. U.S. Patent Application: 10/618,543; July 11, 2003\n'},{id:"B21",body:'Batenburg KJ, Sijbers J. Adaptive thresholding of tomograms by projection distance minimization. Pattern Recognition. 2009;42(10):2297-2305. DOI: 10.1016/j.patcog.2008.11.027\n'},{id:"B22",body:'Kashanipour A, Milani N, Kashanipour A, Eghrary H. Robust color classification using fuzzy rule-based particle swarm optimization. IEEE Congress on Image and Signal Processing. 2008;2:110-114\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Yousif Mohamed Y. Abdallah",address:"y.yousif@mu.edu.sa",affiliation:'
Radiological Science and Medical Imaging Department, College of Applied Medical Science, Majmaah University, Saudi Arabia
Medical Equipments Technology Department, College of Applied Medical Science, Majmaah University, Saudi Arabia
'}],corrections:null},book:{id:"8125",type:"book",title:"Medical Imaging",subtitle:"Principles and Applications",fullTitle:"Medical Imaging - Principles and Applications",slug:"medical-imaging-principles-and-applications",publishedDate:"November 27th 2019",bookSignature:"Yongxia Zhou",coverURL:"https://cdn.intechopen.com/books/images_new/8125.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78923-872-3",printIsbn:"978-1-78923-871-6",pdfIsbn:"978-1-78985-724-5",isAvailableForWebshopOrdering:!0,editors:[{id:"259308",title:"Dr.",name:"Yongxia",middleName:null,surname:"Zhou",slug:"yongxia-zhou",fullName:"Yongxia Zhou"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"47447",title:"Dr.",name:"Milan",middleName:null,surname:"Ambrozic",email:"milan.ambrozic@uni-mb.si",fullName:"Milan Ambrozic",slug:"milan-ambrozic",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{id:"16766",title:"Memory Effects in Mixtures of Liquid Crystals and Anisotropic Nanoparticles",slug:"memory-effects-in-mixtures-of-liquid-crystals-and-anisotropic-nanoparticles",abstract:null,signatures:"Marjan Krašna, Matej Cvetko, Milan Ambrožič and Samo Kralj",authors:[{id:"47441",title:"Dr.",name:"Marjan",surname:"Krašna",fullName:"Marjan Krašna",slug:"marjan-krasna",email:"marjan.krasna@uni-mb.si"},{id:"47446",title:"Dr.",name:"Matej",surname:"Cveto",fullName:"Matej Cveto",slug:"matej-cveto",email:"matej.cvetko@rra-mura.si"},{id:"47447",title:"Dr.",name:"Milan",surname:"Ambrozic",fullName:"Milan Ambrozic",slug:"milan-ambrozic",email:"milan.ambrozic@uni-mb.si"},{id:"177727",title:"Dr.",name:"Samo",surname:"Kralj",fullName:"Samo Kralj",slug:"samo-kralj",email:"Samo.Kralj@um.si"}],book:{id:"424",title:"Ferroelectrics",slug:"ferroelectrics-physical-effects",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"14762",title:"Dr.",name:"Mohd Kamil",surname:"Abd Rahman",slug:"mohd-kamil-abd-rahman",fullName:"Mohd Kamil Abd Rahman",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"25116",title:"Prof.",name:"Marija",surname:"Kosec",slug:"marija-kosec",fullName:"Marija Kosec",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jožef Stefan Institute",institutionURL:null,country:{name:"Slovenia"}}},{id:"29831",title:"Dr.",name:"Rafael",surname:"Hovhannisyan",slug:"rafael-hovhannisyan",fullName:"Rafael Hovhannisyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"32811",title:"Dr.",name:"Hana",surname:"Uršič",slug:"hana-ursic",fullName:"Hana Uršič",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"37702",title:"Mr.",name:"Martun",surname:"Hovhannisyan",slug:"martun-hovhannisyan",fullName:"Martun Hovhannisyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"357554",title:"Dr.",name:"Rajan",surname:"Murgan",slug:"rajan-murgan",fullName:"Rajan Murgan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"357555",title:"Dr.",name:"Junaidah",surname:"Osman",slug:"junaidah-osman",fullName:"Junaidah Osman",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"357723",title:"Dr.",name:"Hovakim",surname:"Alexanyan",slug:"hovakim-alexanyan",fullName:"Hovakim Alexanyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"357724",title:"Dr.",name:"Berta",surname:"Petrosyan",slug:"berta-petrosyan",fullName:"Berta Petrosyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"471248",title:"Dr.",name:"Abdel-Baset",surname:"Ibrahim",slug:"abdel-baset-ibrahim",fullName:"Abdel-Baset Ibrahim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/471248/images/21912_n.jpg",biography:"Abdel-Baset M. A. Ibrahim received a Ph.D. in Nonlinear Optics (2009) from the University Sains Malaysia (Malaysia). Prior to that, he obtained B.Sc. (in Physics) and M.Sc. (in Quantum Optics) degrees from Al-Zagazig University (Egypt) and the University of Malaya (Malaysia), respectively. During 2000–2005, he was a research officer at Telekom Research and Development (Malaysia) working on optical materials and devices. Since 2010, he has been a Senior Lecturer at the Universiti Teknologi MARA (UiTM; Malaysia), and currently, he is the head of the Photonic Research and Materials group over there. He was a visiting research fellow at Abu Dhabi University (UAE) from September 2018 till December 2020. He has supervised more than 12 M.Sc. and Ph.D. students in the areas of quantum optics, nonlinear optics, and condensed matter physics. He has over 50 publications in international journals and book chapters. He reviews for several photonic-based journals. He is a member of Optica (formerly OSA).",institutionString:null,institution:{name:"Universiti Teknologi MARA",institutionURL:null,country:{name:"Malaysia"}}}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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This change influences one another at various temporal and spatial scales; however, improper land uses are the primary causal factor on climate change. It studies relevant literature and Nepal’s case to assess the relationship between land use and climate change. Similarly focuses on how land-use impacts climate change and vice versa. In recent centuries land-use change significant effects on ecological variables and climate change. Likewise, understanding the research on both topics will help decision-makers and conservation planners manage land and climate.",book:{id:"10754",slug:"the-nature-causes-effects-and-mitigation-of-climate-change-on-the-environment",title:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment",fullTitle:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment"},signatures:"Pawan Thapa",authors:[{id:"349566",title:"M.Sc.",name:"Pawan",middleName:null,surname:"Thapa",slug:"pawan-thapa",fullName:"Pawan Thapa"}]},{id:"42926",title:"Disaster Risk Management and Social Impact Assessment: Understanding Preparedness, Response and Recovery in Community Projects",slug:"disaster-risk-management-and-social-impact-assessment-understanding-preparedness-response-and-recove",totalDownloads:10017,totalCrossrefCites:3,totalDimensionsCites:10,abstract:null,book:{id:"3364",slug:"environmental-change-and-sustainability",title:"Environmental Change and Sustainability",fullTitle:"Environmental Change and Sustainability"},signatures:"Raheem A. 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The consequences of greenhouse effect and climate change from rising temperatures, frequent droughts, irregular rainfall, etc. are already evident. Insects and plants are affected by climate change and extreme weather events and the direct impact of anthropogenic climate change has been reported on every continent, in every ocean and in most major taxonomic groups. In the modern period, as a result of natural cycles and anthropogenic activities and their effects on the global climate, plants are typically susceptible to new environmental factors, i.e. higher levels solar radiation, rise in temperatures, greenhouse effect and changes in rainfall patterns over the seasons. Increased temperatures, CO2 and rapid changes in rainfall patterns can dramatically alter the biochemistry of plants and thus plant defence responses. This can have important implications in insect fertility, feeding rates, survival, population size, and dispersal. The relationships between plants and insects are thus changed with significant consequences for food security and natural ecosystems. Similarly, mismatches between plants and insect pollinators are caused by the acceleration of plant phenology by warming. Human nutrition which depends on insect pollination can be affected with reduction in plant reproduction and fitness. Thus, understanding abiotic stress reactions in plants and insects is relevant and challenging in agriculture. In the preparation and implementation of effective strategies for future insect pest management programmes, the impact of climate change on crop production, mediated by changes in the populations of extreme insect pests should be carefully considered.",book:{id:"10754",slug:"the-nature-causes-effects-and-mitigation-of-climate-change-on-the-environment",title:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment",fullTitle:"The Nature, Causes, Effects and Mitigation of Climate Change on the Environment"},signatures:"Somala Karthik, M.S. Sai Reddy and Gummudala Yashaswini",authors:[{id:"416107",title:"Ph.D. Student",name:"Somala",middleName:null,surname:"Karthik",slug:"somala-karthik",fullName:"Somala Karthik"},{id:"416115",title:"Prof.",name:"M.S.",middleName:null,surname:"Sai Reddy",slug:"m.s.-sai-reddy",fullName:"M.S. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. 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This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/47447",hash:"",query:{},params:{id:"47447"},fullPath:"/profiles/47447",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()