Image Processing for Automated Diagnosis of Cardiac Diseases PDF
Preview Image Processing for Automated Diagnosis of Cardiac Diseases
Image Processing for Automated Diagnosis of Cardiac Diseases Image Processing for Automated Diagnosis of Cardiac Diseases Edited by Kalpana Chauhan Department of Electrical Engineering, Central University of Haryana, Mahendragarh, India Rajeev Kumar Chauhan Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India AcademicPressisanimprintofElsevier 125LondonWall,LondonEC2Y5AS,UnitedKingdom 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom Copyright©2021ElsevierInc.Allrightsreserved. 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Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluatingandusing anyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuchinformationormethods theyshouldbemindfuloftheirownsafetyandthesafetyofothers,includingpartiesforwhomtheyhavea professionalresponsibility. Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assumeanyliability foranyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability,negligenceorotherwise,or fromanyuseoroperationofanymethods,products,instructions,orideascontainedinthematerialherein. LibraryofCongressCataloging-in-PublicationData AcatalogrecordforthisbookisavailablefromtheLibraryofCongress BritishLibraryCataloguing-in-PublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary ISBN978-0-323-85064-3 ForinformationonallAcademicPresspublications visitourwebsiteathttps://www.elsevier.com/books-and-journals Publisher:MaraConner AcquisitionsEditor:TimPitts EditorialProjectManager:ChiaraGiglio ProductionProjectManager:SojanP.Pazhayattil CoverDesigner:MilesHitchen TypesetbySPiGlobal,India Contributors Megha Agarwal Department of Electronics and Communication Engineering, Jaypee Institute ofInformation Technology, Noida, India Rajeev Agrawal DepartmentofElectronicsandCommunicationEngineering,G.L.BajajInstituteofTechnologyand Management,GreaterNoida, India V. Ajantha Devi AP3 Solutions,Chennai, Tamil Nadu, India M.A.Ansari Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India Arun Balodi Department of Electronics and Communication Engineering, Atria Institute ofTechnology, Bangalore, India Kalpana Chauhan Department of Electrical Engineering, CentralUniversity ofHaryana, Mahendragarh, India Rajeev Kumar Chauhan Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India I. Lakshmi Department of ComputerScience,StellaMaris College, Chennai, India Rajat Mehrotra Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India Amol D. Rahulkar DepartmentofElectricalandElectronicsEngineering,NationalInstituteofTechnology,Goa,India Anju Saini Department of Mathematics, GraphicEraUniversity, Dehradun, India AswiniK. Samantaray DepartmentofElectricalandElectronicsEngineering,NationalInstituteofTechnology,Goa,India AmitSinghal Department of Electronics and Communication Engineering, BennettUniversity, GreaterNoida, India PragatiTripathi Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India T. Vani Department of ComputerScience,RajeswariVedachalamGovernment Arts College [Affiliated to University ofMadras],Chengalpattu,Tamil Nadu, India xi Preface Thefieldofmedicalimageprocessingisexpandingdaily,asisitsuseinindustrialandmedicalfields. There are many challenges and opportunities in image processing methods and ongoing research is examininghowtousethesemethodstoautomaticallydiagnosediseases.Thisbookexaminesthecur- rentandemergingtechnologiesdevelopedfortheautomateddiagnosisofcardiacdiseases.Thecon- ceptsoutlinedinthisbookcanbetestedforresearchpurposesandthenewadvancesinalgorithmscan beappliedinpracticalapplications.Readerswilllearnsomeofthetechniquesusefulforobtainingim- agesoftheheart.Thebookpresentsbasicaswellasadvancedconceptsofimageprocessingtechniques. Chapter1discussesdifferentheartdiseases,includingirregularitiesthatinfluencethenormalfunc- tioningoftheheartvalves,theheart’selectricalsystem,andthemusclesandcoronaryarteriesofthe heart.Thefocusofthischapterisheartvalvediseases,especiallythoserelatedtothemitralvalve.In particular,thechapterexaminesthediagnosis,causes,andsymptomsofmitralregurgitation(MR).Itis shownthat echocardiographyis the superior imagingtechnique inthis disease. Chapter2dealswithmachinelearning(ML)proceduresincardiovascularmultimodalimaging.In particular,thechapterproposesconvolutionneuralnetwork(CNN)modelsforfeaturingthecorrespon- dences between multimodal information. These portrayals are additionally expected to visualize the cardiovascular life structures in more detail for better understanding and investigation. In addition, thechapterexamineshowquantitativeinvestigationcanbenefitwhenthesescholarlyimageportrayals are utilized indivision, movement following,and multimodal image registration. Chapter3depictsthecardiacanatomyindetailforbetterunderstandingandstudy.Inadditiontoan- atomicalstudy,thechapterdiscusseshowquantitativeresearchcanbenefitfromtheuseoftrainedimage representations in segmentation, motion tracking, and multimodal image registration. A probabilistic edge-maprepresentationisimplementedtodefineanatomicalcorrespondenceinmultimodalcardiacim- ages and to demonstrate its use in spatial image alignment and anatomical localization. In addition, a novelimagesuper-resolutionsystemisimplementedtoimprovecardiaccinemaMRimages. Chapter4offersabriefdescriptionofthetheoreticalstructuresandtheirapplicationsforsegmental cardiac imaging, image enhancement, and multimodal image alignment. These analytical methods sharecommongoals:timeefficiency,quantitativeobjectiveevaluation,andenhancementandanalysis ofmultimodalimage data.Inthissurvey,the authors concentrateonhow thelearningofimage rep- resentation will accomplish these goals and improve the accuracy and robustness of the techniques applied. Chapter5describesfuzzy-baseddespecklingmethodsforechocardiographicimages.Theauthors proposeandanalyzehybridfuzzyfiltersthatintegrateanon-localmeans(NLM)filterwiththreedif- ferenttypesoffuzzyfilters.Theauthorscomparetheproposedmethodswithfifteendespecklingfilters onstandardtestimagesandechocardiographicimagesofthemitralvalveinthreeviews.Theperfor- mance of one proposed hybrid fuzzy filter, HFF3, exhibited the best performance compared to the othersinterms of edge preservation and denoisingof speckle noise. Chapter6examinesmachinelearning-basedmedicaldiagnosisasafast,non-invasive,timesaving, andaccuratemethod.Asthismethodisnon-invasive,itispreferredoverexistingmethods.Thechapter explains the concept of machine learning and its significance in the medical diagnosis of cardiac diseases. xiii xiv Preface Chapter7examinestheuseofvariouswavelettransformsincontent-basedimageretrievalfordi- agnosisofcardiacdiseases.Itdiscusseswaveletpropertiesandanalyzesretrievalperformanceofvar- ious orthogonal, bi-orthogonal, and Gabor wavelet transforms. The authors evaluate the different wavelettransformsusing several cardiacimage databases, namely, NEMA,OASIS, and EXACT09, interms ofaverage retrieval precision(ARP) andaverage retrieval rate (ARR). Chapter8illustratesbroadlyconstructedcomputer-aidedapproachesforevaluatingECGsignals. Artificialintelligencetechniquesgivepreciseandmechanicalclassificationsofheartbeatstoidentify arrhythmias orunexpected changes incardiac morphology.These techniquesare also used for auto- matic syndrome analysis, monitoring, and stratification by managing extended ECG recordings for which diagram and physical investigations can be monotonous and time consuming. AI is flexible andcanbepracticallyutilizedinwearableECGdevices,assuringcompetentanddependablemonitor- ingoftheheartinbothclinicalandresidentialsettings.Thechapteralsoexamines3Dcomputersim- ulationsas influential apparatuses for understanding ECG results. Chapter9proposesanewregularizationmodelfordetectingECGimageboundaries.Themethod helpsthecurvetoapproachthedesiredboundarieswhilemaintainingsmoothnessforbettervisualiza- tion.Theauthorsuseregion-basedsegmentationalongwithspeckledensityasthedatafittingenergyto determine intensity information in local regions. The proposed improved regularization and fitting- basedsegmentation(IRFS)techniquewithanewregularizationmodelandfittingfunctionsuccessfully achievedtherightminimaandregionalongwithimprovedcapabilityofthecurvetodrawthedesired boundaries. Chapter10considersapubliclyavailabledatasetofcine-MRI(magneticresonanceimaging)im- agestodetectheartfailurecaseswith(orwithout)infarction.Localtexture-basedpatternsareusedto extract relevant information from the image. The chapter examines four different types of pattern- based features: local binary pattern (LBP), local ternary pattern (LTP), difference of Gaussian LTP (DoGLTP), and ternary co-occurrence pattern (LTCoP). Various machine learning classifiers are employedtodifferentiatebetweennormalheartimagesandheartfailureimages.Performancemetrics arecomputedfortheseclassificationstrategiesandadetailedcomparisonisprovidedtohighlightthe most accurate method for automated identification of heart failure. Chapter11isaboutthefusionmethodadoptedinthediagnosisofcardiacdiseases.Theadvance- mentsinmedicalimagefusionresearchoutlinedinthischapterdemonstratetheimportanceoffusionin improvingcardiacdiagnosis,monitoring,andvisualization.Thealgorithmsusedforcardiacimagefu- sionmethodscanimproveimagequalityandcanbeusedindifferentapplications.Theprominentap- proaches tested on cardiac images include discrete wavelets transform (DWT), principle component analysis (PCA), and maximum model. The performance of the methods shows that the combination ofoneor moremethods ofimage fusion is effective incardiac image analysis. Acknowledgment Thankstooursupporterduringtheeditingofthisbook.Editingabookisharderthanwethoughtand more rewarding than we could have ever imagined. This would have not been possible without the adjustmentmadebyoursonShauryaChauhan.Hehascooperatedalotandgiveshiscontinuousemo- tional supportduringthisjourney. We areeternally grateful toourparents,who taughtusdiscipline,love,manners, respect, andso muchmorethathavehelpedustosucceedinlife.Theyeverencouragedustoworkhard.Wewouldlike tothankallourfamilymembersandfriendsfortheirdirectandindirectsupport.Thankstothedoctors andhospitalsthathaveprovidedrealimagesanddatafortheresearch.Thankyoutoallthecontributors who have added their researchin the form ofchaptersin thisbook. Thanks toeveryonein ourpublishing team. xv CHAPTER 1 Cardiac diseases and their diagnosis methods Kalpana Chauhanaand Rajeev Kumar Chauhanb DepartmentofElectricalEngineering,CentralUniversityofHaryana,Mahendragarh,Indiaa DepartmentofElectricalEngineering,DayalbaghEducationalInstitute,Agra,Indiab Chapter outline 1.1 Introduction .....................................................................................................................................2 1.2 Heartvalves ....................................................................................................................................2 1.3 Mitralvalveregurgitation .................................................................................................................3 1.4 Heartdiseases .................................................................................................................................4 1.4.1 Coronaryarterydisease(CAD) .......................................................................................4 1.4.2 Myocardialinfraction(MI) ............................................................................................5 1.4.3 Highbloodpressureorhypertension(HBP) ....................................................................5 1.4.4 Heartvalvedisease ......................................................................................................5 1.4.5 Cardiomyopathyorheartmuscledisease ........................................................................5 1.4.6 Pericarditis .................................................................................................................5 1.4.7 Rheumaticheartdisease(RHD) ....................................................................................5 1.5 Mitralvalvediseases .......................................................................................................................5 1.5.1 Mitralregurgitation(MR) ..............................................................................................6 1.5.2 Causesofmitralregurgitation .......................................................................................6 1.5.3 Mitralregurgitationsignsandsymptoms ........................................................................6 1.5.4 Mitralregurgitationdiagnosis ........................................................................................7 1.6 Cardiacdiseasediagnosismethods ...................................................................................................7 1.6.1 Principlesofecho ........................................................................................................9 1.6.2 Modesofechocardiography...........................................................................................9 1.6.3 Two-dimensionalrecordingtechniques ........................................................................14 1.6.4 Advantagesandlimitationsofechocardiography ...........................................................15 1.7 Resultsandanalysis .....................................................................................................................15 1.8 Discussion ....................................................................................................................................16 1.9 Conclusions ..................................................................................................................................17 References ..........................................................................................................................................17 1 ImageProcessingforAutomatedDiagnosisofCardiacDiseases.https://doi.org/10.1016/B978-0-323-85064-3.00011-X Copyright#2021ElsevierInc.Allrightsreserved. 2 CHAPTER 1 Cardiac diseases and their diagnosis methods 1.1 Introduction Theheartisamuscularstructureandacentralcomponentofthevertebratecardiovascularsystem.The heart functions in a closed loop manner, that is, oxygenated blood is pumped from the lungs to the wholebodyanddeoxygenatedbloodispumpedbackfromthelungstothebody.Thetransferofblood fromhearttothebodyiscarriedoutbythearteriesandarterioles,whilethereturningofthebloodis donethroughthevenulesandveins.Bloodtransportisvitaltobringoxygenandnutrientstothebody’s tissuesas well asto remove carbon dioxide and waste products/chemicals [1]. Thehumanheartislocatedbetweenthelungs.Becauseofslighttiltingofitsapexontheleftsideof thechest,heartrhythmorbeatingoccursinthislocationcausinganillusionthattheheartislocatedon thatside.Thesizeofahumanheartisthatofatightlyclosedfist.Itbeatsabout100,000timesinaday. Although the heart pumps blood, delivering oxygen to the entire body’s muscles and organs for them tofunction,italsoneeds itsown oxygen-enrichedblood toworkproperly.Theheartfunctions asalargemuscularpumpwitharteries,veins,andvalves,andanelectricalsystem.Theelectricalsys- temtriggerspulse,therebystimulatingthehearttobeat.Theheartmusclesthensqueezethebloodto push the oxygenated blood throughout the entire body in one large arterial circuital system and the deoxygenatedbloodthroughthepulmonaryarteriestothelungs.Thetwo,one-wayvalvescreatesep- arationbetweenthefourdifferentchambers,namely,theleftventricle(LV)andleftatrium(LA),and rightventricle(RV)andrightatrium(RA),forformingthedualpumpsoftheheartadjustingbothrate andflowoftheoxygenatedanddeoxygenatedbloodthroughouteachcardiaccycleorheartbeat.The moreactivityapersonperforms,themoretheheartmusclesmustworktosupplythenecessaryquantity ofblood tothe musclesto beutilized during the activity. Mitral regurgitation (MR) is a mitral valve insufficiency that causes a change in the size and/or shapeoftheLV,affectingitsfunctioningandresultingfromischemicheartdisease[2,3].MRleads tomyocardialinfarction(MI)inabout20%ofcases[4,5].TheseverityofMRincreasesaround30%in patients suffering from coronary artery disease (CAD) with ischemic LV dysfunction [6]. TherearemanyapproachesavailabletodiagnoseMRthatarehelpfulindeterminingseveritygrade anddysfunction[7–13].Diagnosticmethodsincludeassessmentofregurgitationvolume,orificesize, orifice,andregurgitantorificewiththehelpofechocardiographyorcatheterization.Inaddition,two- dimensional (2D) contrast echocardiography and Doppler echocardiography are efficient ways for assessingMR.We discuss the advantages ofthese techniques later in the chapter [14–20]. To begin, this chapter discusses different heart conditions by categorizing heart valves and their relateddiseases,withaspecialfocusonMR.Italsopresentsvariousdiagnosticmethodsandthequal- itativeandquantitativeparametersusefulingradingMRseverity.Finally,thechapterendswithadis- cussion ofdifferent modes and techniques ofechocardiography. 1.2 Heart valves Thetwoatrioventricular(AV),one-wayvalvesarethinstructures,havingconnectivetissuesanden- docardia.Thesevalves,namely,thebicuspid/mitralandthetricuspidAVvalvesarelocatedbetween theLAandtheLV,andtheRAandRV,respectively.Thetwosemilunar,one-wayvalvesaremadeup ofthreeflaps,eachcomposedofconnectivetissuesandendocardiumaswellasfiberstopreventthe valvesfromflappinginsideout.Theirshapesarelikeahalfmoonandthustheyarecalledthesemilunar 1.3 Mitral valve regurgitation 3 FIG.1.1 Classificationofheartvalves. (SL)aorticvalveandSLpulmonaryvalve.Thesevalvesarelocatedbetweentheleftventricleandaorta andbetweentheRVandthestartofpulmonaryartery.Fig.1.1showsthesevalves.Theheart’sone-way bloodflowismaintainedwiththehelpoffourheartvalves,eachonehavingaspecificpositiononthe exitsofthefourchambers.Thesefourheartvalvesallowonlytheone-wayflowofbloodintheforward directionsandrestrictthebackwardflowofblood.Sequenceofbloodflowisfromtheatria(rightand left) into the ventricles (right and left) through the open tricuspid and mitral valves, respectively, as shown in Fig. 1.1. According to pressure change in the chambers, there is an opening or closing of AV valves. They close during the ventricular systole (contraction) when the ventricle pressure in- creasesthepressureinthetwoatria.Thisactionkeepsthevalvessnappedshutandpreventsbackward flowofblood.Thecontractionoftheventriclesleadstoforcedopeningofthepulmonaryandaortic valves to pump the blood from the right and left ventricles into the pulmonary artery (through open valves)towardsthelungs,andthroughtheaorticvalvetotheaortaandthebody.Attheendofcon- traction,theventriclesbegintorelaxandtheaorticandpulmonicvalvesremainclosedduringthedi- astole. Backward flow ofblood into the ventriclesis preventedbythesevalves.This pattern repeats againand again, causing continuous blood flow fromthe hearttothe lungs andthe body. 1.3 Mitral valve regurgitation Tovisualizethemitralvalve(MV),cliniciansmustchooseatechniquethatenhancestheimageaccord- ingtotheirvisualperceptionandthatworksinaccordancewiththekindofimage[21].Logtransfor- mation does not give satisfactory results (subjective assessment) in the contrast enhancement of echocardiographicimagesduetohighwhitepixelspreading.Thiswhitespreadingoverlapstheimpor- tantfeatures.Thereasonforthisproblemisthatmorepixelswillshiftinthehigh-intensityvaluewhen thelogtransformationisapplied.Toovercomethisproblem,thefigureof1inEq.(1.1)oflogtrans- formationisreplacedbyavariable,say,a.Thisoffersaflexiblewaytoanalyzetheimageatdifferent values of a. This value can be changed by clinicians in accordance with their visual perceptions for bettervisualizationoftheimage.ThenormalMVopenswhentheLVrelaxes(diastole)toallowblood flowfromthe LA and tofill the LV (decompressed). DuringsystoleorcontractionoftheLV,thepressureintheLVincreases.Thisincreasedpressure leadstoclosureoftheMVandrestrictsbloodflowfromleakingintotheLA.Atthistime,theblood flowstotheaorta (passingthe aorticvalve)andthebody.Theannulus,leaflets,andsubvalvularap- paratusesworkinacomplexmannerfortheproperfunctioningofthevalve.Themitralleaflettissues
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