Clinical indications for peripheral blood film.
\r\n\t
",isbn:"978-1-80355-403-7",printIsbn:"978-1-80355-402-0",pdfIsbn:"978-1-80355-404-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"360fe5dabd12a1f91a5658a5fe3eff66",bookSignature:"Associate Prof. Murat Eyvaz and Dr. Ahmed Albahnasawi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11934.jpg",keywords:"Hydrogen Sources, Hydrogen Production, Hydrogen Safety, Hydrogen Storage Methods, Environmental Impacts of Hydrogen, Synthetic Fertilizer Production, Aromatization, Hydrocracking, Hydrodesulfurization, Fuel Cells, Gas Turbines, Hydrogen Driven Vehicles",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 18th 2022",dateEndSecondStepPublish:"March 18th 2022",dateEndThirdStepPublish:"May 17th 2022",dateEndFourthStepPublish:"August 5th 2022",dateEndFifthStepPublish:"October 4th 2022",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Eyvaz is a pioneering researcher in environmental sciences and engineering, who has co-authored numerous journal articles and conference papers and has four patents on wastewater treatment systems.",coeditorOneBiosketch:"Dr. Albahnasawi is a pioneering researcher in environmental sciences and engineering, who has co-authored numerous journal articles and conference papers on water and wastewater treatment and waste remediation.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"170083",title:"Associate Prof.",name:"Murat",middleName:null,surname:"Eyvaz",slug:"murat-eyvaz",fullName:"Murat Eyvaz",profilePictureURL:"https://mts.intechopen.com/storage/users/170083/images/system/170083.png",biography:"Dr. Murat Eyvaz is an associate professor in the Environmental Engineering Department, Gebze Technical University, Turkey. His research interests include applications in water and wastewater treatment facilities, electrochemical treatment processes, filtration systems at the lab and pilot-scale, membrane processes (forward osmosis, reverse osmosis, membrane bioreactors), membrane manufacturing methods (polymeric membranes, nanofiber membranes, electrospinning), spectrophotometric analyses (UV, atomic absorption spectrophotometry), chromatographic analyses (gas chromatography, high-pressure liquid chromatography). He has co-authored many journal articles and conference papers and has taken part in many national projects. He serves as an editor and reviewer for many indexed journals. 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Based on his Ph.D. research, Dr. Albahnasawi published three journal articles and participated in three international conferences. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"67667",title:"Erythrocyte Morphology and Its Disorders",doi:"10.5772/intechopen.86112",slug:"erythrocyte-morphology-and-its-disorders",body:'\nErythrocytes are the major cellular component of the circulating blood. Roughly, erythrocytes in circulation average about 5 million cells per cubic millimetres of blood. With an average life span of about 100–120 days, erythrocyte production and senescence is maintained in constant equilibrium. Any imbalances affecting production or destruction of red cells result in red cell disorder. In essence, red cells are maintained at a constant volume in the body, depending on several factors. Physiologic factors such as age, sex, altitude, smoking status or pregnancy account for slight inter-individual and intra-individual variations. Typically, there are different measures of red cell counts and they include red cell mass, red cell volume, red cell count, haematocrit and haemoglobin concentration. Red cell volume or mass is expected to fall within an interval of mean ± 2 SD within a specified population for a person’s age, sex and race.
\nBeyond count anomalies (quantitative abnormalities), morphologic aberrations (qualitative abnormalities) are highly relevant in clinical evaluation of red cell diseases. Normally, a red cell has a round form, shaped like a disc, well-haemoglobinised cytoplasmic rim with a central pallor covering inner third of the red cell. Deviations in morphology (size, shape, colour, contents/inclusion or distribution) may be associated or perhaps diagnostic of disease entities. For instance, a blood picture with paucity of red cells, numerous red cell fragments, increased polychromatic red cells suggests a micro-angiopathy or fragmentation syndrome.
\nThis chapter aims to discuss principles of red cell morphology, as well as describe red cells in terms of morphology, identify morphologic abnormalities associated with different disease conditions.
\nCirculating red cells are formed from bone marrow stem cells. Stem cells are pluripotent; they self-replicate and differentiate to specialized cells in circulation through different lineages. Red cells are formed from the myeloid stem cell lineage (colony forming unit—granulocytes, erythroid, myeloid and megakaryocytes). The earliest recognizable red cell precursor in the bone marrow is the pronormoblast. The pronormoblast undergoes series of maturation to become the orthochromatic normoblast. Upon extrusion of its nucleus, the late normoblast becomes the shift reticulocytes, which is released into the circulation. Finally, DNA remnants and other chromatin materials in the reticulocytes is removed by the pitting action of the spleen, hence the mature red cells.
\nErythrocytes cannot be seen with the naked eyes. Typically, morphology of red cells is performed on peripheral blood smears, once there is an indication. Erythrocyte morphology is either indicated by a clinical request or laboratory flags. Examples of clinical indications for peripheral blood film/erythrocyte morphology are listed in Table 1.
\nA clinical request for a PBF may be prompted by the following indications: | \n
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Clinical indications for peripheral blood film.
Erythrocyte morphology may also be indicated when significant deviations from the normal are seen in the laboratory during blood work (full blood count) irrespective of a clinical request. For instance, a significantly reduced haemoglobin level with low MCV and raised RDW may suggest iron deficiency anaemia. This is an indication for red cell morphology and other ancillary investigation for iron deficiency.
\nBlood for peripheral blood film is collected through venipuncture. Anticoagulant of choice is the potassium EDTA. Specimens should be analysed as soon as possible, preferably within 2 hours of blood collection. Samples not analysed immediately should be stored at 2–6°C in a refrigerator, or the blood smear should be made, dried and fixed, for subsequent staining with Romanowsky dyes.
\nAsides automated slide makers, the commonest method for preparation of peripheral blood film is the slide ‘wedge’ or push technique. This technique typically requires microscope slides, pipette/blood dropper, spreader slide and the blood specimen to be analysed. Standard precautions must be observed to prevent transmission of infectious pathogens such as human immunodeficiency virus and hepatitis viruses.
\nQuality control measures will include ensuring proper anticoagulant: blood ratio, sample processing/analysis within sample viability period and adequate mixing of the blood before smearing. Each slide must be labelled with at least two patient identifiers such as name and laboratory, and date of procedure. Once the smear is air-dried in about 5–10 minutes, fixation of the blood tissue is another very important step. Fixation helps to preserve the architecture of the cells, which ensures good morphology. A dried slide should be fixed within 4 hours of preparation, preferably in the first hour.
\nFor routine morphology, the glass slides are stained with Romanowsky dyes. Romanowsky dyes are differential stains composed of both acidic and basic components. The acidic component is eosin and the basic part is azure B or polychrome methylene blue. Examples of Romanowsky stains include Leishman stain, Jenner, Wright stain, May-Grunwald-Giemsa stain and Giemsa stain. Generally, the eosin part of the dye binds to the basic component of the cell such as the haemoglobin molecules in the red cell and stains it pink. The basophilic part of the dye binds to the acidic component of the cells such as the nucleus and stains it blue. Other components of the cells appear in different colour shades that contrasts and compares with the dye. The term, azurophilic is used to describe a neutral to sky-blue colour shade. For instance, the cytoplasm of a neutrophil is described as azurophilic in colour. Furthermore, the characteristic staining quality of different red cell components is presented in Table 2.
\nCell component | \nColour | \n
---|---|
Chromatin (including H-J bodies) | \nPurple | \n
Cytoplasm with RNA and nuclear Remnants (e.g. polychromasia, basophilic stippling’s) | \nIn polychromatic red cells, RNA produces a blue colour, which offsets the pink colour imparting a purple tinge Basophilic stippling: appears as blue granules dispersed within the cytoplasm in a punctate appearance | \n
Mature red cells | \nPink | \n
Romanowsky staining characteristics of red cell components.
Staining procedure and the stain contact time depends on the type of dye in use. Staining protocols are contained in standard laboratory texts and reagent manuals. Red cell morphology should be examined at the monolayer region of the film which is 2–4 × 10 fields from the feathered edge. In this place red cells are randomly distributed with most lying singly and only a few overlapping. If area is too thin, the RBCs will appear flat with no central pallor. If too thick, false rouleaux may be reported and morphology may be difficult to evaluate because red cells are packed.
\nThe haemato-morphologist reviews the red cell morphology under the compound microscope and notes any significant abnormalities for reporting/diagnosis in light of patient clinical context. Red cell morphology is evaluated in terms of size, shape, colour, distribution and intra cytoplasmic inclusions. In general, red cells have a fairly uniform variation in size, with a red cell distribution width of 11–15% in normal individuals. Abnormal variations in sizes and shape are termed anisocytosis and poikilocytosis, respectively [1].
\nNormal red cells (normocytes) are about 7–8 μm in diameter [2]. Reduced size is termed microcytosis. Increase in red cell diameter above normal is called macrocytosis. Red cell sizes form the basis for morphologic or cytometric classification of anaemia. In terms of red cell size, anaemia could be described as microcytic, normocytic or macrocytic. Typically, the normal red cell size is adjudged by comparison with the nucleus of a small lymphocyte. The reference interval for mean red cell volume (MCV) is 80–95 fl [3, 4]. MCV >95 fl is termed macrocytic. While, red cell size <6 μm and/or MCV <80 fl is termed microcytic [5]. Differentials of microcytic anaemias include iron deficiency, thalassemias, sideroblastic anaemia and anaemia of chronic inflammation (20% of cases). Further test such as serum ferritin, total iron binding capacity (TIBC), haemoglobin electrophoresis with quantification helps to differentiate microcytic anaemia [4, 6]. For instance, low serum ferritin, raised TIBC and raised RDW is expected in iron deficiency. A normal or elevated red cell counts with little red cell size variation (RDW) in the presence of microcytosis is suggestive of a thalassaemia.
\nNormocytic anaemia occurs in acute blood loss, marrow aplasia, anaemia of chronic disease (80% of cases) and anaemias of endocrine origin. Macrocytosis may be oval or round, with specific casual relationships. Oval macrocytes are seen in megaloblastic anaemias (folate/cobalamin deficiencies), myelodysplastic syndrome and drug therapies such as hydroxyurea [7]. Round macrocytes are seen in liver disease and excess alcohol use. MCV may appear falsely normal with the haematology analyser in combined substrate deficiency states. However, the blood picture will reveal marked anisopoikilocytosis. The red cell distribution width (RDW) is a calculated parameter and it measures the individual size variability (heterogeneity) of the red cells. RDW is the percentage coefficient of variation of the individual red cell volumes enumerated by the particle counter [8]. RDW normally ranges between 11.5 and 15.5%. For interpretation purposes, raised RDW is seen in iron deficiency anaemia, megaloblastic anaemia (folate and cobalamin deficiency), haemolytic anaemia, recent blood transfusion, hereditary spherocytosis and sickle cell syndromes [8, 9]. RDW is useful in interpreting apparently normal MCV since it will be quite high in combined micronutrient deficiency state.
\nShape abnormalities, otherwise called poikilocytes are useful pointers to specific diagnosis. It is important to note that poikilocytosis may also occur in vitro (artefactual causes). It is therefore necessary to ensure adequate precautions in reducing pre-analytic and intra-analytic errors that affects morphology. As a reminder, the following quality control measures apply in blood film morphology:
Blood specimens for PBF are best collected in EDTA bottles through venipuncture.
Optimal blood: anticoagulant ratio should be observed.
Samples should be dispatched immediately to the haematology laboratory. Prolonged delay in analysis allows for cellular degeneration, pseudo-thrombocytopenia and artefactual changes [10].
Blood specimens for morphology are best analysed within 2 hours of collection.
Poikilocytes are categorized as either spiculated or non-spiculated. Spiculated red cells have at least one pointed projection from the cell surface. Examples of spiculated poikilocytes are burr cells, schistocytes (red cell fragments), irreversibly sickled red cells (drepanocytes), acanthocytes and tear drop red cells (dacrocytes). Non-spiculated poikilocytes include target cells, ovalocytes and stomatocytes. Various mechanical, biochemical and molecular mechanisms underlie pathologic changes in red cell shape. Some occur as a result of disturbances in the haematopoietic system. Target cells have an area of central haemoglobinization (termed hyperchromic bull eyes) surrounded by a halo of pallor. Increased red cell surface area to volume ratio in target cells is due to its redundant membrane, which gives rise to the targetoid shape. Target cells (Figure 1) are seen in sickle haemoglobinopathies, thalassemias, iron deficiency and post splenectomy state. Tear drop red cells (Figure 2) results from abnormal spleen or bone marrow pathology such as primary myelofibrosis when the red cells stretch out in order to navigate its way into the periphery or as a result of stretching from the pitting action of the spleen, when red cells with inclusions such as Heinz bodies navigates the splenic cords into the sinuses [5].
\n(1) Nucleated red cell, (2) target cell, and (3) irreversibly sickled red cell.
(4) Tear drop red cell.
Stomatocytes have a fish mouth appearance (slit-like central pallor). They are mostly due to increased red cell permeability, resulting in increased volume. Stomatocytes may be inherited or acquired. Hereditary stomatocytosis is seen in Rh null phenotype. Acquired stomatocytosis is mostly seen with recent excessive alcohol and typically resolves within 2 weeks of alcohol withdrawal. When artefactual, stomatocytes are usually <10% of the red cell population. As the name implies, irreversibly sickled red cells (Figure 1) are seen in sickle syndromes. The primary event is intra-erythrocytic haemoglobin precipitation (gelation), with resultant formation of tactoids, which deforms the discoid red cell to sickle or crescent morphology [11]. Burr cells are seen in renal failure and may be artefactual. Artefactual red cells may be caused by poor fixation and high humidity in the laboratory ambience. Artefactual tear drop cells should be suspected if the tails line up in the same direction. Table 3 itemizes common poikilocytes and its differentials [1, 5, 12, 13, 14, 15].
\nRed cell shapes | \nDifferential diagnosis | \n
---|---|
Irreversibly sickled red cells (drepanocytes) | \nSickle cell syndromes (SS, SC, S-β-thalassemia) | \n
Target red cells (codocytes) Target cells (codocytes, Mexican hat cells) | \nSickle cell disease, haemoglobin C trait, haemoglobin CC disease, thalassemia’s, iron deficiency, liver disease (cholestasis), asplenia | \n
Fragmented red cells (schistocytes, helmet cells, keratocytes) | \nThrombotic micro-angiopathic haemolytic anaemias such as disseminated intravascular coagulopathy (DIC), thrombotic thrombocytopenic purpura, haemolytic uraemic syndrome. | \n
Pencil cells | \nIron deficiency | \n
Stomatocytes | \nArtefact (due to slow drying in humid environment), liver disease, alcoholism, Rh-null disease, obstructive lung disease | \n
Elliptocytes | \nHereditary elliptocytosis (>25%) | \n
Bite cells (degmacytes) | \nG6PD deficiency, oxidative stress, unstable haemoglobin’s, congenital Heinz body anaemia | \n
Basket cells (half ghost cells/blister cells) | \nOxidant damage, G6PD deficiency, unstable haemoglobin’s | \n
Spherocytes | \nHereditary spherocytosis, ABO incompatibility, autoimmune haemolytic anaemia (warm antibody type), severe burns | \n
Teardrop red cell (dacrocytes, lacrymocytes) | \nIdiopathic myelofibrosis, myelophthisic anaemia, thalassemia’s | \n
Red cell shape anomalies and associated diseases.
Anisochromia depicts increased or decreased haemoglobinization of the red cells. In hypochromic red cells, the central pallor exceeds one third of the diameter. Hypochromia usually follows microcytosis, as seen in iron deficiency states. Hyperchromia (increased haemoglobinisation) is associated with shape abnormalities such as (micro)-spherocytes and sickled red cells. Increased haemoglobinization obliterates central pallor. Occasionally, severe hypochromia is associated with macrocytic red cells, termed leptocytes. Leptocytes are seen in severe iron deficiency, thalassemia and liver diseases [14]. Polychromasia on PBF suggests in-vivo reticulocytosis. Literally, polychromasia means ‘many colours’, i.e. the red cells bear another shade of colour than pink (eosinophilic). Polychromatic red cells are macrocytic (young red cells) and have a bluish tinge. The blue tinge denotes the presence of rRNA which eventually undergo the pitting action of the spleen to become mature circulating red cells [1]. Normally, polychromatic red cells are not obvious on PBF—adult reticulocyte population is about 0.5–2.5% [3]. However, polychromatic red cells in excess of 1–2% in the periphery should be considered significant since normal daily rate of red cell turnover is about 1–2% [16]. In situations of acute haemorrhage, haemolysis, and high altitude, hypoxia induces increased erythroid activity, hence polychromasia. Polychromasia is also seen in extramedullary haemopoiesis due to myeloid metaplasia in reticulo-endothelial tissue. Following haematinic therapy, polychromatic red cells are seen as a response to treatment of micronutrient deficiency [1].
\nSimilarly, in severe situations causing marrow stress, nucleated red cells (erythroblastosis) exit the bone marrow prematurely in order to compensate. Notable causes of erythroblastosis (or normoblastemia) include severe anaemia, asplenic/hyposplenic state as in sickle cell disease, severe hypoxia, marrow replacements or infiltrations and extramedullary haemopoiesis [17, 18]. In neonates, nucleated red cells are normally seen in the periphery [15].
\nOther morphologic abnormalities include presence of inclusion bodies and pathologic distribution of red cells on the smear. A mature erythrocyte lacks inclusion bodies. Red cell inclusion bodies include nuclear products RNA/DNA, haemoglobin or iron pigments. Some, such as haemoglobin H inclusions and Heinz bodies can only be appreciated with supravital staining. Red cell inclusions result from oxidant stress, severe infections and dyserythropoiesis (maturation defects). Basophilic stipplings or punctuate basophilia are denatured RNA fragments dispersed within the cytoplasm. Basophilic stipplings may be fine, blue stipplings or coarse granules. They are non-specific and are generally related to disorders in haem biosynthetic pathways [1, 19]. Differentials include haemoglobinopathies (thalassemias), lead or arsenic poisoning, unstable haemoglobins, severe infections, sideroblastic anaemia, megaloblastic anaemia and a rare inherited condition, pyrimidine 5′ nucleotidase deficiency [1, 10, 20].
\nClinically insignificant, fine basophilic stippling may be associated with polychromasia/accelerated erythropoiesis/reticulocytosis. Coarse stipplings are clinically significant and indicates impaired haemoglobin synthesis as seen in megaloblastic anaemia, thalassemias, sideroblastic anaemias and lead poisoning [1, 19]. Unlike other basophilic inclusions such as Howell jolly bodies and Pappenheimer bodies which tend to be displaced to the periphery, basophilic stipplings are diffusely dispersed throughout the red cell cytoplasm. Howell jolly bodies (Figure 3) are DNA remnants seen in post-splenectomy patients, anatomical or functional asplenia. Siderotic granules or Pappenheimer bodies appear purple on Romanowsky stain, blue on Perl’s stain and are seen in disorders of iron utilization like sideroblastic anaemias.
\n(5) Howell Jolly body (in a 36-year-old lady with sickle cell disease).
Parasites such as
Rouleaux formation refers to stacking of red cells like coins in a single file. Rouleaux is seen in hyperproteinaemias. Elevated plasma fibrinogen or globulins reduces the zeta potential (repulsive force) between circulating red cells, facilitating their stacking effect. Rouleaux is associated with myeloma/paraproteinaemias, other plasma cell disorders as well as B cell lymphomas. On the other hand, agglutination refers to clumping or aggregation of red cells into clusters or masses and is usually antibody mediated [1]. Agglutination of red cells may be seen in cold haemagglutinin disease and Waldenstrom’s macroglobulinaemia [1, 11]. Agglutination is associated with falsely reduced red cell count and high MCV. Pre warming the specimen with heating block helps to disperse the red cells prior to making of a blood smear and automated cell counts.
\nRed cell morphology is crucial in evaluating anaemias and several blood disorders. Good quality smear, with proper Romanowsky/special staining, coupled with the expertise of an haemato-morphologists (haematologists/haematology pathologists) remains highly valuable in patient care.
\nBiometrics is a technology that uses physical and/or behavioral characteristics of people to identify them. Systems of this type implement two processes (Figure 1) [1]:
Enrollment
Authentication
Biometric recognition system.
The physical features are fingerprints, hand geometry, handprint, facial image, iris, retina, and ear. Behavioral features are signature, lip motion, speech, dynamics of typing, hand movements, and gait.
\nThe characteristics of effective biometrics are:
Unique features for each individual
Invariant traits over time (e.g., due to the effect of aging)
Features that are relatively easy to obtain (computational complexity small)
Precise algorithms enabling classification
Resistance to various types of attacks
Low cost
Ease of implementation
The security of the biometric system is usually assessed on the basis of some indicators. These are:
False match rate (FMR). It belongs to the group of matching errors. This indicator is defined as the expected probability that the downloaded sample will be falsely matched to the template in the database, but it will not be the test user pattern. If the indicator is high, it means that there is a risk that an unauthorized person will be recognized as a system user.
False rejection (FRR) is equivalent to the FMR. The difference between these indicators is that FMR refers to a single match, and the FRR refers to a situation where one or more attempts to match a sample to a template from the database may occur. The FRR error is referred to in the literature as type I error.
False discrepancy (FNMR). This is the coefficient determining the probability that the sample taken will not be matched to the pattern in the database belonging to the user from whom the sample was taken. In biometric verification (1:1) systems, the indicator means that the sample has not been identified by a specific pattern, while in biometric identification systems (1:N), this indicator determines the probability that a given pattern will not be found in the database.
The false acceptance factor (FAR) is equivalent to the FNMR indicator. The difference between him and FNMR is the same as between FRR and FMR.
Equal error rate (EER). It is defined as the intersection of the FAR and FRR characteristics in the graph of the dependence of these errors on the threshold of sensitivity (t). This factor indicates the optimal sensitivity threshold at which the same number of people is incorrectly rejected and incorrectly accepted. The lower the EER error value, the better the biometric system is.
The FMR (FRR) and FNMR (FAR) parameters can also be represented by graphs (Figure 2):
Receiver operating characteristic (ROC) curve showing the dependence of FNMR on FMR. You can use it to show the accuracy of the system.
Detection error trade-off (DET) showing error rates on both axes, most often on a logarithmic scale. This curve is plotted for both matching errors and decisions (Figure 2).
The graph of FAR, FRR, and EER in receiver operating characteristic (ROC) curve.
If we use only one biometric authentication system, the results obtained are not always good enough. Unimodal biometric systems using a single sensor have many limitations, such as lack of uniqueness, universality, and lack of interference level associated with the acquired data, as a result of which they are unable to provide the required level of identification/verification efficiency (Table 1). This is due to the fact that the reliability of the biometric modality applied is affected by the precision of a single biometric system (Table 2).
\nName | \nDescription | \n
---|---|
Distortion of the input biometric data | \nDistorted biometric data may prevent the correct alignment process with database templates, as a result of which users are incorrectly rejected or identified | \n
Intra class variations | \nBiometric data obtained from the person during authentication may differ from the data used to generate the template during registration, thus affecting the matching process. The biometric template should have a small intra-class variance | \n
Interclass similarities | \nBiometric features should be significantly different for different people and should ensure small similarities between classes in the feature space. There is an upper limit to the users who can be effectively distinguished by any biometric system. The capacity of the identification system cannot be arbitrarily increased for fixed sets of feature vectors and the matching algorithm. The biometric template should have large interclass variations | \n
Non-universality | \nObtaining accurate (useful) biometric data from the users is not always possible | \n
Intruder attacks | \nAttacks of this type involve the manipulation of biometric features to avoid recognition. It is also possible to create artificial biometric patterns in order to accept the identity of another person | \n
Limitations of unimodal biometrics.
Name | \nDescription | \n
---|---|
Recognition accuracy | \nThe multi-biometric system ensures greater accuracy and reliability thanks to many independent biometric features that are difficult to attack | \n
Continuous monitoring | \nIn case when one biometric modality is obstructed, other modalities of the multi-biometric system ensure correct user identification | \n
Privacy | \nMulti-biometric systems provide greater resistance to certain types of loopholes and attacks. It is difficult and/or impossible to steal many biometric patterns (templates) stored in the biometric database | \n
Biometric data enrollment | \nWhen biometric input data is unavailable or unacceptable by a biometric system, another biometric system modality may be used | \n
Resistance on spoof attacks | \nUsually the attacker is not able to use many relevant (accurate) spoofed biometrics | \n
Advantages of multi-biometric systems.
The multi-biometric system can be (Figure 3) (a) a multi-sensor system that allows obtaining data from various sensors using one biometric feature, (b) a system with multiple algorithms processing a single biometric feature, (c) a system consolidating multiple occurrences of the same body trait, (d) a system using multiple templates of the same biometric method obtained with the help of a single sensor, and (e) a multimodal system combining information about the biometric features of the individual to establish his identity [2, 3, 4].
\nTypes of multi-biometric systems.
In multimodal biometric systems, there are a number of strategies (scenarios) for the fusion of biometric information:
Data fusion from sensors. Data from various sensors form one vector. Fusion of information obtained from many different sensors for a single biometric feature.
The fusion of feature vectors extracted from various biometric modalities for further processing. A merger of information obtained from several unimodal biometric systems that process different body characteristics of the same person (Figure 4a).
Fusion at the decision level. The merger of decisions developed on the basis of information from different biometric modalities, and the resultant feature vector defines two main classes, i.e., rejection or acceptance (Figure 4b).
Rank level fusion. The classifier determines the rank of each registered biometric identity. A high position is a good indicator of a good fit (Figure 4b).
Levels of fusion. (a) Feature level fusion and (b) score/rank level fusion.
The fusion of biometrics modalities on different levels of multi-biometrics system is extensively studied in the literature (Table 3). For all that the merger at the level of feature vectors is relatively poorly discussed. The merger at this level includes the integration of feature vectors corresponding to many sources of information. Because the feature vectors contain more elements than the input biometric data, it is obvious that the merger at the feature vectors level will provide better authentication results. However, mergers at this level are difficult to implement in practice because (i) sets of features of many modalities may be incompatible, (ii) the combination of two feature vectors may result in a vector of features with very large dimensionality, and (iii) a complex comparing system is required.
\nBiometrics traits | \nFusion methods | \nDescription of the implementation method | \nReferences | \n
---|---|---|---|
Fingerprint and face | \nIn [5], it was proposed to extract face and fingerprint characteristics invariant to the rotation and scaling of Zernike moments (ZM). On the basis of ZM, the fusion of facial features and fingerprints is realized. The RBF network implements the decision-making process. The accuracy rate is 96.55%. Testing result of authentication rate are FAR, 4.95%, and FRR, 1.12% | \n[5] | \n|
Score | \nIn [6], authors presented score level fusion technique using the SIFT features for the face and the minutiae features for fingerprint. Results are: FAR = 1.98%, FRR = 3.18%, and accuracy = 97.41 | \n[6] | \n|
Fingerprint, finger knuckle print, finger vein Finger shape | \nThe multi-set canonical correlation analysis is used to fuse multiple feature sets. The feature based on MCCA achieves the recognition performance, with EER = 2.3900e-04 | \n[1] | \n|
With the help of the unified Gabor filter, fingerprint codes and finger vein codes are generated. The extraction of features is carried out by using a supervised local canonical correlation analysis (SLPCCA), and finally the NN-classifier is used | \n[7] | \n||
Fingerprint and iris | \nScore | \nIn [8], authors propose a frequency approach to generate a unified homogeneous template for fingerprint and iris features. Scores generated from these templates are fused using the sum rule | \n[8] | \n
Palm print and hand shape | \nInformation from the face image and gait image are combined at the function level. Using the principal component analysis (PCA) method, facial features were obtained The result of multiple discrimination analysis (MDA) is gait energy image (GEI) Recognition rate results are 91.3% | \n[9] | \n|
Palm print and iris | \nIn system described in [10], texture parameters are extracted based on Gabor filters. | \n[10] | \n|
Fusion of the palm print features and iris features is based on the wavelets. Decision is obtained using kNN classifier. Recognition accuracy is 99.2% and FRR = 1.6% | \n|||
\n | In [11], fusion method for the information of phases about the iris and palm utilizes a Baud limited image product (BLIP) | \n[11] | \n|
Finger knuckle and palm print | \nIn this paper, feature extraction method for palm print is monogenic binary coding; for inner knuckle print recognition, two algorithms named ridgelet transform and SIFT are proposed. The extracted feature vectors are classified using SVM | \n[12] | \n|
Palm print and face | \nThe PCA is used to extract features of palm and face images. Fusion technique concatenated the feature vectors of the face and palm modalities into one fused vector, and feature selection is performed. | \n[13] | \n|
Face and gait | \nMethod is based on learning face and gait features in image transform spaces. Two methods are considered—PCA and LDA | \n[14] | \n|
Face and iris | \nScore | \nMulti-biometrics system using dual iris, visible and thermal face traits is considered. 1D Log-Gabor and Complex Gabor Jet Descriptor (CGJD) were used to extract feature vectors. Authors proposed a score level fusion algorithm | \n[15] | \n
The ordinal measures and local binary pattern (LBP) methods are proposed to extract features from iris and face regions, respectively | \n[16] | \n||
Paper [17] presents the extractions of iris features based on 2D Gabor and facial features using the PCA method | \n[17] | \n||
Face and hand geometry | \nThe 2D DCT is used to extract discriminant face features which are concatenated with hand geometric features. The resultant feature vector is classified using SVM | \n[18] | \n|
Face and ear | \nScores | \nTo match score level, fusion is proposed in [19]. Authors use Dempster-Shafer decision theory for each modality. Recognition rate is 95.53% with 4.47% EER | \n[19] | \n
Ocular—iris and conjunctival vascular | \nScore | \nIn [4], authors presented fusion of both iris and conjunctival vascular information. A weighted fusion method is proposed for each modality. The fusion resulted in an EER of 2.83% | \n[4] | \n
Face, ear, and signature | \nRank | \nIn [20], the PCA and Fisher’s linear discriminant (FLD) methods in the face, ear, and signature, multimodal biometrics system is proposed. Local features are extracted from face, ear, and signature data. Features are matched using Euclidean distance. This system is using rank level fusion | \n[20] | \n
Summary of works on multimodal biometric systems.
The multi-biometric system (dorsal vein + periocular + palm print) is presented in Figure 5.
\nConsidered multi-biometric system architecture.
In our proposed method, the first is preprocessing block including noise elimination, ROI detection and normalization, and contrast normalization. For all three modalities, noise elimination for an image \n
where \n
Next step in preprocessing phase is ROI detection and normalization (Figure 6). This operation is quite different for dorsal vein images, palm print images, and periocular images. For dorsal vein images, we use distance transform to detect the dorsal image center and build square ROI based on this center coordinates [22, 23]. The ROI design for palm print images is based on hand-specific points (finger valleys) and two angles [24]. The periocular region is detected based on the center of the iris. Using the conventional algorithm for detecting the iris, we determine the center of the iris and its diameter. The periocular area is a rectangle centered in the iris center [25, 26].
\nROI area for dorsal vein images (a), palm print images (b), and periocular images (c).
After the ROI detection, we perform image size normalization and apply the contrast normalization by using CLAHE algorithm. The image is divided into non-overlapping areas of equal size, and the histograms of each region are calculated. Next, the cutoff threshold for histograms is obtained, and each histogram is processed in such a way that its height does not exceed the cutoff threshold [21].
\nThe sample input images after normalization operations and operations using the CLAHE algorithm are shown in Figure 7. Next processing blocks include feature extraction, feature selection, fusion, and classification.
\nImages after normalization (size 150 × 150 pixels) and after applying the CLAHE algorithm.
In biologically inspired vision models, receptor fields exist that are the primary aspect of early visual processing in mammalian vision systems. Gabor functions are widely used in image feature analysis because they are similar to receptive field profiles in mammalian cortical simple cells. These fields are modeled using Gabor filters [27].
\nImitation of mammalian vision systems (or some of them) in object recognition systems leads to their increased efficiency and plausibility. Object recognition systems that are inspired by the biological approach use filter banks, in particular Gabor filters (Figure 8) [28, 29, 30, 31, 32].
\n2D functions and 2D Gabor filter.
The 2D Gabor filter family can be represented as expressed in Eq. (2):
\nwhere \n
The \n
\n\n
Gabor response images are obtained by convolution operation of multiscale and multi-orientation Gabor filters \n
where and\n
The Gabor filter responses for palm print image and dorsal vein image are shown in Figures 9 and 10, respectively.
\nImaginary part of the Gabor filter responses of a palm print image.
Imaginary part of the Gabor filter responses of a dorsal vein image.
The periocular area contains the iris, eyes, eyelids, eyelashes, and partially eyebrows. The
\n
\n
The
where\n
The idea of this operator is presented in Figure 11.
\nThe basic idea of LBP approach.
For an image size \n
where
Using the
Typically image is divided into
In the case \n
The original image (a) and image as a result of the LBP operator (b).
The
The multi-biometric system has been tested using certain parts of the following databases: PolyU palmprint [24], IIITD periocular database [25], and Bosphorus hand vein database [35]. We choose 20 subjects with 10 images per subject at random. From 10 images, 5 images are used for training and 5 for the testing.
\nThe combination of feature vectors at this level is difficult to achieve in practice due to the combination of certain fundamentally different feature vectors that can result in a resulting vector of features with very large dimensionality. In a merger at the level of feature vectors, each individual modality process generates a feature vector. The fusion process combines these feature vectors into one vector.
\nFor dorsal vein images and for palm print images, we perform the same image processing operations that the feature vectors have the same sizes. As a result of convolution operation of multiscale and multi-orientation Gabor filters with the input image, we get the Gabor response images. The feature vector has a very large size of
For periocular images, the feature vector has a size of 36 × 59 = 2124.
\nNext we reduce dimensionality of these vectors used in PCA method (Figure 14 and Table 4) [5]. Separated features are normalized using zero mean and unit variance as
\nSteps to image processing using PCA.
PCA algorithm | \n
Organizing the training set of images \n\n where | \n
Calculating the average of the set T \n\n | \n
Calculating \n\n | \n
Calculating the covariance matrix C \n\n | \n
The eigenvectors and corresponding eigenvalues are computed \n\n | \n
The eigenvectors and their corresponding eigenvalues are paired and ordered from high to low. Approximated image is calculated as \n\n for \n | \n
PCA algorithm.
where \n
\nTable 5 shows the recognition performance depending on the number of selected eigenvectors.
\nModality | \nNumber of the eigenvectors | \n|||
---|---|---|---|---|
k = 40 | \nk = 60 | \nk = 80 | \nk = 100 | \n|
Dorsal vein | \n88 | \n89.3 | \n91.4 | \n92.6 | \n
Palm print | \n88.7 | \n89.3 | \n90.6 | \n92.8 | \n
Periocular | \n86 | \n86.8 | \n89 | \n89.2 | \n
Dorsal vein + palm print | \n90.3 | \n91.1 | \n92.3 | \n93.1 | \n
Dorsal vein + periocular | \n91.1 | \n92 | \n92.4 | \n92.8 | \n
Palm print + periocular | \n90.7 | \n91.4 | \n91.8 | \n92.1 | \n
Recognition rates [%] for different modality.
In this chapter, Gabor’s functions and LBP features are proposed for recognition in a multi-biometric system that uses three modalities: dorsal vein, periocular, and palm print. Using PCA method dimensionality feature vectors from these modality are reduced. Feature vectors are normalized and fused using concatenation operation. Based on the results, we suggest that multi-biometric system using the fusion of dorsal vein, periocular, and palm print images can offer recognition rate which the unimodal biometric system cannot.
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\n\nSve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
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Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization"},{id:"26",title:"Machine Learning and Data Mining",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence"},{id:"27",title:"Multi-Agent Systems",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. 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We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. 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