Detailed Table of Contents Foreword.............................................................................................................................................. xv Preface.................................................................................................................................................xvi Acknowledgment................................................................................................................................. xx Chapter 1 PrototypeofaLow-CostImpedanceTomographyBasedontheOpen-HardwareParadigm................. 1 David Edson Ribeiro, Universidade Federal de Pernambuco, Brazil Valter Augusto de Freitas Barbosa, Universidade Federal de Pernambuco, Brazil Clarisse Lins de Lima, Universidade Federal de Pernambuco, Brazil Ricardo Emmanuel de Souza, Universidade Federal de Pernambuco, Brazil Wellington Pinheiro dos Santos, Universidade Federal de Pernambuco, Brazil ElectricalImpedanceTomography(EIT)isanoninvasive,painless,andionizingradiation-freetechnique forimageacquisitionofaregionofinterest.Itisperformedthroughelectricalparameters.Themethod isbasedontheapplicationofanalternatingelectriccurrentpatternoflowintensitythroughelectrodes arrangedaroundthesurfaceregioninordertoobtaintheimageandalsotomeasuretheexcitation ,electricalpotentials.Theaimofthisstudyistodevelopadevicebasedinopenhardware.Furthermore theauthorsaimtobuildaprototypeofadataacquisitionsystembasedonEIT.Thisdeviceisthefirst .steptoobtainacompleteandportabletomographyequipmentatalowcost Chapter 2 UsingExtremeLearningMachinesandtheBackprojectionAlgorithmasanAlternativeto ReconstructElectricalImpedanceTomographyImages...................................................................... 16 Juliana Carneiro Gomes, Escola Politécnica, Universidade de Pernambuco, Brazil Maíra Araújo de Santana, Universidade Federal de Pernambuco, Brazil Clarisse Lins de Lima, Universidade Federal de Pernambuco, Brazil Ricardo Emmanuel de Souza, Universidade Federal de Pernambuco, Brazil Wellington Pinheiro dos Santos, Universidade Federal de Pernambuco, Brazil ElectricalImpedanceTomography(EIT)isanimagingtechniquebasedontheexcitationofelectrodepairs appliedtothesurfaceoftheimagedregion.Theelectricalpotentialsgeneratedfromalternatingcurrent excitationaremeasuredandthenappliedtoboundary-basedreconstructionmethods.Whencomparedto ,otherimagingtechniques,EITisconsideredalow-costtechniquewithoutionizingradiationemission saferforpatients.However,theresolutionisstilllow,dependingonefficientreconstructionmethods   andlowcomputationalcost.EIThasthepotentialtobeusedasanalternativetestforearlydetectionof breastlesionsingeneral.Themostaccuratereconstructionmethodstendtobeverycostlyastheyuse ,optimizationmethodsasasupport.Backprojectiontendstoberapidbutmoreinaccurate.Inthiswork theauthorsproposeahybridmethod,basedonextremelearningmachinesandbackprojectionforEIT reconstruction.Theresultswereappliedtonumericalphantomsandwereconsideredadequate,with .potentialtobeimprovedusingpostprocessingtechniques Chapter 3 ClassificationofBreastLesionsinFrontalThermographicImagesUsingaDiagnosisAid IntelligentSystem................................................................................................................................. 28 Maíra Araújo de Santana, Universidade Federal de Pernambuco, Brazil Jessiane Mônica Silva Pereira, Universidade Federal de Pernambuco, Brazil Clarisse Lins de Lima, Universidade Federal de Pernambuco, Brazil Maria Beatriz Jacinto de Almeida, Universidade Federal de Pernambuco, Brazil José Filipe Silva de Andrade, Universidade Federal de Pernambuco, Brazil Thifany Ketuli Silva de Souza, Universidade Federal de Pernambuco, Brazil Rita de Cássia Fernandes de Lima, Universidade Federal de Pernambuco, Brazil Wellington Pinheiro dos Santos, Universidade Federal de Pernambuco, Brazil This study aims to assess the breast lesions classification in thermographic images using different configuration of an Extreme Learning Machine network as classifier. In this approach, the authors changedthenumberofneuronsinthehiddenlayerandthetypeofkernelfunctiontofurtherexplorethe networkinordertofindabettersolutionfortheclassificationproblem.Authorsalsouseddifferenttools .toperformfeaturesextractiontoassessbothtextureandgeometryinformationfromthebreastlesions Duringthestudy,theauthorsfoundthattheresultschangednotonlyduetothenetworkparametersbut alsoduetothefeatureschosentorepresentthethermographicimages.Amaximumaccuracyof95% .wasfoundforthedifferentiationofbreastlesions Chapter 4 FeatureSelectionBasedonDialecticalOptimizationAlgorithmforBreastLesionClassification inThermographicImages..................................................................................................................... 47 Jessiane Mônica Silva Pereira, Universidade Federal de Pernambuco, Brazil Maíra Araújo de Santana, Universidade Federal de Pernambuco, Brazil Clarisse Lins de Lima, Universidade Federal de Pernambuco, Brazil Rita de Cássia Fernandes de Lima, Universidade Federal de Pernambuco, Brazil Sidney Marlon Lopes de Lima, Universidade Federal de Pernambuco, Brazil Wellington Pinheiro dos Santos, Universidade Federal de Pernambuco, Brazil Breastcanceristheleadingcauseofdeathamongwomenworldwide.Earlydetectionandearlytreatment arecriticaltominimizetheeffectsofthisdisease.Inthissense,breastthermographyhasbeenexplored ,intheprocessofdiagnosingthistypeofcancer.Furthermore,inanattempttooptimizethediagnosis intelligentpatternrecognitiontechniquesarebeingused.Featuresselectionperformsanessentialtaskin thisprocesstooptimizetheseintelligenttechniques.Thischapterproposesafeaturesselectionmethod usingDialecticalOptimizationMethod(ODM)associatedtoaKNNclassifier.Theauthorsfoundthat thiscombinationprovedtobeagoodapproachshowingalowimpactonbreastlesionclassification performance.Theyobtainedaround5%decreaseinaccuracy,withareductionofabout46.80%ofthe featuresvector.Thespecificityandsensitivityvaluestheyfoundwerecompetitivetootherwidelyused methods.  Chapter 5 :ComputerTechniquesforDetectionofBreastCancerandFollowUpNeoadjuvantTreatment UsingInfraredExaminations................................................................................................................ 72 Adriel dos Santos Araujo, Fluminense Federal University, Brazil Roger Resmini, Federal University of Rondonópolis, Brazil Maira Beatriz Hernandez Moran, Fluminense Federal University, Brazil Milena Henriques de Sousa Issa, Federal Hospital of Servants of Rio de Janeiro, Brazil Aura Conci, Fluminense Federal University, Brazil Thischapterexploresseveralstepsofthethermalbreastexamsanalysisprocessindetectingbreast abnormalityandevaluatingtheresponseofpre-surgicaltreatment.Topicsconcerningtheprocessof acquiring,storing,andpreprocessingtheseexams,includinganovelsegmentationproposalthatuses collective intelligence techniques, will be discussed. In addition, various approaches to calculating .statisticalandgeometricdescriptorsfromthermalbreastexaminationsarealsoconsideredofthischapter ,Thesedescriptorscanbeusedatdifferentstagesoftheanalysisprocessoftheseexams.Inthissense twoexperimentswillbepresented.Thefirstoneexplorestheuseofgeneticalgorithmsinthefeature ,selectionprocess.Thesecondconductsapreliminarystudythatintendstoanalyzesomedescriptors alreadyusedinotherworks,intheprocessofevaluatingpreoperativetreatmentresponse.Thisevaluation .isoffundamentalimportancesincetheresponseisdirectlyassociatedwiththeprognosisofthedisease Chapter 6 BreastCancerDiagnosisWithMammography:RecentAdvancesonCBMR-BasedCADSystems. 107 Abir Baâzaoui, SIIVA, LIMTIC Laboratory, Institut Supérieur d’Informatique El Manar, Université de Tunis El Manar, Tunisia Walid Barhoumi, Ecole Nationale d’Ingénieurs de Carthage, Université de Carthage, Tunisia & SIIVA, LIMTIC Laboratory, Institut Supérieur d’Informatique El Manar, Université de Tunis El Manar, Tunisia Breastcancer,whichisthesecond-mostcommonandleadingcauseofcancerdeathamongwomen,has witnessedgrowinginterestinthetwolastdecades.Fortunately,itsearlydetectionisthemosteffective waytodetectanddiagnosebreastcancer.Althoughmammographyisthegoldstandardforscreening,its .difficultinterpretationleadstoanincreaseinmissedcancersandmisinterpretednon-cancerouslesionrates Therefore,computer-aideddiagnosis(CAD)systemscanbeagreathelpfultoolforassistingradiologists inmammograminterpretation.Nonetheless,thesesystemsarelimitedbytheirblack-boxoutputs,which decreasestheradiologists’confidence.Tocircumventthislimit,content-basedmammogramretrieval CBMR)isusedasanalternativetotraditionalCADsystems.Herein,authorssystematicallyreview) the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advancesinCBMRmethods.Inordertohaveacompletereview,mammographyimagingprinciples