Image Processing for Automated Diagnosis of Cardiac Diseases

222 Pages·2021·20.192 MB·English

by  Rajeev Kumar Chauhan (editor), Kalpana Chauhan (editor)

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
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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