ICTMI 2017: Proceedings of the International Conference on Translational Medicine and Imaging

220 Pages·2019·8.23 MB·English

by  Balázs Gulyás, Parasuraman Padmanabhan, A. Lenin Fred, T. R. Santhosh Kumar, Sundramurthy Kumar

Balázs Gulyás · Parasuraman Padmanabhan 
A. Lenin Fred · T. R. Santhosh Kumar 
Sundramurthy Kumar    Editors
ICTMI 2017
Proceedings of the International
Conference on Translational Medicine
and Imaging
ICTMI 2017
á á
Bal zs Guly s Parasuraman Padmanabhan
(cid:129)
A. Lenin Fred T. R. Santhosh Kumar
(cid:129)
Sundramurthy Kumar
Editors
ICTMI 2017
Proceedings of the International Conference
on Translational Medicine and Imaging
123
Editors
BalázsGulyás T. R.Santhosh Kumar
Lee KongChianSchool ofMedicine Rajiv Gandhi Centrefor Biotechnology
NanyangTechnological University Thiruvananthapuram, Kerala, India
Singapore, Singapore
SundramurthyKumar
Parasuraman Padmanabhan Lee KongChianSchool ofMedicine
Lee KongChianSchool ofMedicine NanyangTechnological University
NanyangTechnological University Singapore, Singapore
Singapore, Singapore
A.LeninFred
Mar Ephraem Collegeof Engineering
andTechnology
Kanyakumari,Tamil Nadu, India
ISBN978-981-13-1476-6 ISBN978-981-13-1477-3 (eBook)
https://doi.org/10.1007/978-981-13-1477-3
LibraryofCongressControlNumber:2018948591
©SpringerNatureSingaporePteLtd.2019
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Contents
AnAutomatedFrameworkforPredictionofFallsinCardiomyopathy
People. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Pasupuleti Megana Santhoshi and Mythili Thirugnanam
Synthesis, Characterization, and MRI Properties
of Cysteamine-Stabilized Cadmium Zinc Selenide (Cd(Zn)Se)
Quantum Dots for Cancer Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
J. Joy Sebastian Prakash and Karunanithi Rajamanickam
Measures of Diffusion Tensor Tractography of Regions Associated
with Default Mode Network in Alzheimer’s Disease. . . . . . . . . . . . . . . . 29
J. Joy Sebastian Prakash, Karunanithi Rajamanickam and R. M. Arunnath
Noninvasive Quantitative Tissue Biopsy Using Precise Optical
Phantoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
V. Vijayaragavan and N. Sujatha
BAT Optimization-Based Vector Quantization Algorithm
for Compression of CT Medical Images. . . . . . . . . . . . . . . . . . . . . . . . . 53
S. N. Kumar, A. Lenin Fred, H. Ajay Kumar, P. Sebastin Varghese
and Ashy V. Daniel
Study of Polymorphic Ventricular Tachycardia in a 2D Cardiac
Transmural Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Ponnuraj Kirthi Priya and M. Ramasubba Reddy
Finger Movement Pattern Recognition from Surface EMG Signals
Using Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Shravan Krishnan, Ravi Akash, Dilip Kumar, Rishab Jain,
Karthik Murali Madhavan Rathai and Shantanu Patil
v
vi Contents
Parcellation Analysis of Language Areas of the Brain
and Its Clinical Association in Children with Autism
Spectrum Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Beena Koshy, T. Hannah Mary Thomas, Devarajan Chitra,
Anna Varghese, Rachael Beulah and Sunithi Mani
A Step to In Vivo Dosimetry Using Electronic Portal Imaging
Device: Initial Experience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Sangaraju Siva Kumar, Minu Boban, Kaliyaperumal Venkatesan,
Jomon Raphael, Mathew Varghese, R. Murali and N. Arunai Nambi Raj
Natural Lovastatin (NL) as an Anticancer Agent: Docking
and Experimental Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Ganesan Saibaba, Balraj Janani, Rajmohamed Mohamed Asik,
Durairaj Rajesh, Ganesan Pugalenthi, Jayaraman Angayarkanni
and Govindaraju Archunan
Brain Tumor Detection and Classification of MRI Brain Images
Using Morphological Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Mavis Gezimati, Munyaradzi C. Rushambwa and J. B. Jeeva
Significance of MTA1 Expression Status in Progesterone
Responsiveness of Endometrial Cancer Cells . . . . . . . . . . . . . . . . . . . . . 151
J. S. Chithra and S. Asha Nair
Probable Role of Non-exosomal Extracellular Vesicles in Colorectal
Cancer Metastasis to Kidney: An In Vitro Cell Line Based Study
and Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Aviral Kumar, Reetoja Nag, Satyakam Mishra, Bandaru Ramakrishna,
V. V. R. Sai and Debasish Mishra
NIR Reflectance Imaging of Biological Tissue Using Multiple
Sources and Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
J. B. Jeeva, Siddesh Raut, Ameena Yari and C. Jim Elliot
Feature Extraction-Based Hyperspectral Unmixing . . . . . . . . . . . . . . . . 185
M. R. Vimala Devi and S. Kalaivani
A View on Atlas-Based Neonatal Brain MRI Segmentation. . . . . . . . . . 199
Maryjo M. George and S. Kalaivani
Challenges in the Diagnosis of Retinopathy of Prematurity—An
Imaging and Instrumentation Perspective . . . . . . . . . . . . . . . . . . . . . . . 215
J. Mary Annie Sujitha, Priya Rani, E. R. Rajkumar
and P. Arulmozhivarman
An Automated Framework for Prediction
of Falls in Cardiomyopathy People
PasupuletiMeganaSanthoshiandMythiliThirugnanam
Abstract Purpose In medical field, cardiomyopathy is one of the heart muscle
diseases associated with blood pumping that causes heart complications like heart
failures,cardiacarrest,andsuddendeath.AccordingtotheWHO,globallyatleast
onein500issufferingfromcardiomyopathy.Itcanbeidentifiedbysymptomssuch
aschestpain,dizziness,syncopewhichcausesfalling.Atpresent,inthecardiomy-
opathy field, issues such as poor accuracy in detection of cardiomyopathy and no
emphasis on classification of cardiomyopathy types are addressed. Especially, no
workisconcentratedonpredictionoffallduetocardiomyopathy.Hence,thiswork
aims to propose an automated framework for prediction of fall in cardiomyopathy
patients.ProcedureandConclusionThisframeworkconsistsoffivephases,firstand
secondfocusedonimprovingtheECGsignalqualitytoresolveaccuracyproblems.
Thirdistodetectandclassifythecardiomyopathytypeaswellaspredictionoffall.
Infourthandfifth,predictiondetailswillbetransmittedtoWebapplicationandthen
topersonaldevices.
·
Keywords Pre-falldetection Cardiomyopathydetection
· ·
Classificationofcardiomyopathy ECGdataanalysis Bodysensors
Fallwithhealthabnormality
1 Introduction
Cardiomyopathy is a type of heart muscle disease which causes heart valve sizes
dilate and diminish. Because of this reason, heart will face difficulty to pump the
blood,andsometimesitcausesheartfailurestoo.AsperWorldHealthOrganization
statisticsofdeathsrelatedtoheartdiseases,in2012eightytwothousandpeoplewere
gotmortalitywithcardiomyopathy.[1,2]stated22%womenand15%menwithin
B
P.MeganaSanthoshi( )·M.Thirugnanam
SchoolofComputerScienceandEngineering,VITUniversity,Vellore,
TamilNadu,India
e-mail:[email protected]
©SpringerNatureSingaporePteLtd.2019 1
B.Gulyásetal.(eds.),ICTMI2017,https://doi.org/10.1007/978-981-13-1477-3_1
2 P.MeganaSanthoshiandM.Thirugnanam
average age of 56 and 58 are suffering from cardiomyopathy in 714 participants.
In UK and Australia, at least one person in 500 is suffering from cardiomyopathy
disease [3, 4]. In USA, the third dominant cause of heart failure is cardiomyopa-
thy[5].Cardiomyopathydiseaseisbroadlyclassifiedintofourtypeswhichinclude,
i.e.,dilatedcardiomyopathy(DCM),hypertrophiccardiomyopathy(HCM),restric-
tivecardiomyopathy(RCM),andarrhythmogenicrightventricularcardiomyopathy
(ARVC).Alltypesofcardiomyopathyhappenwhenflowofbloodleadstothewhole
body which causes major complications like heart failure, blood clot, valve disor-
ders, cardiac arrest, and sudden death. These complications are notified with the
symptomsoftiredness,swellingintheabdomenandankles,breathlessness,painin
chest,palpitation,syncopeanddizzinessorfainting,etc.[6].Symptomswhichare
discussedarethemainreasonforfallsoccurincardiomyopathypatients.
According to the WHO, the second dominant cause of accidental death is fall.
Itcausesmajorinjurieslikehipfractures,headfractures,andsometimesitleadsto
deathalso.Globally,everyyearapproximately42,400peopledyingduetofalls[7].
Generally,somehouseholdenvironmentslikeslipperytiles,unarrangedfurniture’s,
stairsarethereasonsforthefall.Thereisonemoreimportantreasontofalleventis
abnormalityinhealth.Fallsrelatedtohealthproblemsmayhaveopportunitytoavoid
byanalyzinghumanhealthstatus.Therearemanyreasonstocauseabnormalityin
health. Cardiomyopathy is one of the health problems with dizziness and syncope
symptomsthatcausefalls[8].CardiomyopathycanbediagnosedwithECG,ECHO,
ultrasound,MRIScanning,andstresstest.
Electrocardiogram (ECG) is a fast, safety, painless, and low-cost technique to
detect the heart abnormalities. It is the process of recording the electrical activity
oftheheartinonecardiaccycleviatheelectrodesplacedontheskin.Twelvecon-
ventionalECGmachineshavetenelectrodeswhichareplacedondifferentpartsof
humanbodylikelimbs,arms,andontopofchest.Theseelectrodesrecordcardiac
electricalsignalsinwaveformwithtwodimensionsoffrequencyandtime.TheECG
trace waveform representing the heart rhythm has four entities, i.e., P wave, QRS
complex, T wave, and U wave. P wave indicates the atrium depolarization, QRS
complex indicates the ventricular depolarization, T wave indicates the ventricular
repolarization,andUwaveindicatesmusclerepolarization.Thesefourentitiesare
importantinputsforrecognizingthecardiomyopathycondition.
Thispaperbringstheconceptofcardiomyopathydetectionandclassification,pre-
dictionoffallincardiomyopathypatientsandimplementsoptimalfeaturetechnique
tomitigatetheimpactoffallswiththehelpofECGsignals.Thisworkalsoimproves
thequalityofECGtoachievebetteraccuracyofdiseasedetectionbypre-processing
techniques.
Further,theworkelaboratedinthefollowingstructure.Literaturereviewsection
focusedonreviewofcardiomyopathyandreviewoffalldetection.Andtheproposed
framework and detailed discussionare carried outinproposed framework section.
Andfinalsectionconcludesthepaperwithconclusionandfutureenhancement.
AnAutomatedFrameworkforPrediction… 3
2 LiteratureReview
Surveyexplanationhasbeengivenindividuallybelowondetectionofcardiomyopa-
thyandpre-detectionofafalloccurringaspartofregularactivitiesofapersonlike
walking, sitting, and standing and also occurring of falls due to person abnormal
healthconditions.
2.1 ReviewonCardiomyopathy
Cardiomyopathyisadiseaserelatedtoheart,somedicalstrategieslikeECG,ECHO,
ultrasound videos, MRI images are used in detection of cardiomyopathy [9–25].
Some of the researchers have applied neural networks multilayer perceptron [9],
feed-forward and static back-propagation algorithm [10], support vector machine
[11,16,18,20,21]andfuzzydecisionfunctionalgorithms[11]thatareusedtoclas-
sifytheECGsignalsfordifferentiatethenormalheartandcardiomyopathyaffected
heart.Shukrietal.[14]haveachieved90%accuracyincardiomyopathydetection,
and Mahmood and Syeda-Mahmood [16] achieved 77.8% accuracy in detection
ofhypertrophiccardiomyopathythroughultrasoundimages.Balajietal.[20]have
achieved92.07%accuracyindetectionofbothdilatedcardiomyopathyandhyper-
trophiccardiomyopathybyapplyingimageprocessingtechniquesonECHOvideo
images.
Andreaoetal.[9]classifytheECGsignalsbywaveletclassificationeffectively.
Jadhav et al. [10] proposed an automated classification system for arrhythmias by
testedwith452patients’ECGbio-signalsandconcludedthatsystemworkswellin
boundaryconditions.Kohlietal.[11]discussedabouttheclassificationofECGsig-
nalstodifferentiateischemicchanges,oldinferiormyocardialfraction,sinusbrady-
cardia,rightbundlebranchblockcases.Potteretal.[12]usedadvancedECGtech-
niquestoidentifythehypertrophiccardiomyopathy.Losietal.[13]reviewedtheold
techniquesinechocardiographylikeM-modeimaging,two-dimensionalLVhyper-
trophy,Dopplerechocardiography,andalsonewtechniqueslikecontrastechocardio-
graphy,tissueDopplerimaging,strainrateimaging,andreal-timethree-dimensional
echocardiography to analyzing pathophysiological natures in HCM patients. This
system identified the hypertrophic cardiomyopathy with echocardiography images
and concluded that echocardiography alone cannot differentiate different forms of
unexplainedLVhypertrophy.
Shukri et al. [14] presented an investigation on detection of cardiomyopathy
by using Elman neural networks and achieved 90% accuracy. Drezner et al. [15]
explainedtheECGleaddifferencespresentedinathletestodifferentiatingtheLVNC,
DCM, HCM, and ARVC. Mahmood and Syeda-Mahmood [16] proposed an auto-
matedsystemtodetectdilatedcardiomyopathybyusingcardiacultrasoundimages
andclassifiednormalandabnormalleftventricleswithlessaccuracy.O’Mahonyetal.
[17]discussedtheparametersinhypertrophiccardiomyopathypatientswhichlead
4 P.MeganaSanthoshiandM.Thirugnanam
tosuddencardiacdeathconditionswith3675patients’datasetobservation.Rahman
etal.[18]used12-leadECGsignaltoclassifythehypertrophiccardiomyopathybased
onmorphologicalandtemporalfeaturesof221HCMand541non-HCMpatients’
heartbeats.
TripathyandDandapat[19]proposedasystemtoclassifythecardiacabnormal-
itieslikeheartmuscledefect,bundlebranchblock,healthycontrol,andmyocardial
infarctionover.
ECGsignalssuggestedthatECGheartbeatsignalsareefficientindetectingother
heartdiseaseslikeseptaldefects.Balajietal.[20]determinedatoolthatidentified
thedilatedcardiomyopathyandhypertrophiccardiomyopathybyusingleftventricle
parametersinechocardiogramimageswith92.04%accuracyinclassification.Begum
andRamesh[21]proposedanautomatedsystemtodetectcardiomyopathybyobserv-
ingthe65subjectshealthyandcardiomyopathyECGsignals,achieved95%classifi-
cationaccuracybyneuralnetworksclassification,andalsosuggestedthepossibility
ofdetectingthefourtypesofcardiomyopathywhichincludesdilatedcardiomyopa-
thy,hypertrophiccardiomyopathy,restrictivecardiomyopathy,andarrhythmogenic
rightventricularcardiomyopathy.Waeletal.[22]introducedregionalwallthickness
parameter to detect the myocardium infraction and hypertrophic cardiomyopathy
diseases by using MRI images, but this system generated the disease discriminate
parametersbyconsideringthelimitednumberofsubjects,i.e.,27datasetsarecol-
lected from normal and diseased persons. Several research works have stated on
cardiomyopathydetection,showninTable1.
As executed with the analysis on cardiomyopathy, it is observed that an ECG
signal plays a major role in automatic identification of cardiomyopathy. But raw
ECGcontainssomenoiseslikehigh-frequencynoiseandbaselinenoise.Inanypro-
cess,qualitydatayieldthequalityresult.Inordertoremovethenoise,mostofthe
researchershaveimplementedpre-processingtechniquesbyusingbandpassfilters,
medianfilter,andmovingaveragefilters.However,thosefiltersarenotachievedthe
100%qualitydatawhichleadtolessaccurateintheresult.Anotherobservationis
most of the researchers have committed their research on detection of cardiomy-
opathy disease by extracting various features over ECG like time-based signals,
frequency-based. But these features have given the less accuracy in finding car-
diomyopathy. Hence, an attempt is made to improve the quality of ECG by using
effectivefilteringtechniqueswhichwouldbefurtherprocessedbyselectingoptimal
featurestoimprovetheaccuracyindetectionofcardiomyopathyandclassification
ofcardiomyopathytypes.
2.2 ReviewonPre-fallDetection
Researchonfalldetectionhasachievedthehighaltitudeinemergencynotification
ofelderlypeoplewheneveranaccidentalfallsoccurred.Subsequently,researchers
havestartedtheirworksalsoonpre-falldetectionoverthefallsoccurredinregular
activitiesofanelderlypersontoavoidthefallconsequences.Mostoftheresearchers