Computer Aided Intervention and Diagnostics in Clinical and Medical Images

292 Pages·2019·10.45 MB·English

by  J. Dinesh Peter

Lecture Notes in Computational Vision and Biomechanics 31
J. Dinesh Peter
Steven Lawrence Fernandes
Carlos Eduardo Thomaz
Serestina Viriri    Editors
Computer Aided
Intervention
and Diagnostics
in Clinical and
Medical Images
Lecture Notes in Computational Vision
and Biomechanics
Volume 31
Series editors
João Manuel R. S. Tavares, Porto, Portugal
Renato Natal Jorge, Porto, Portugal
Editorial Advisory Board
Alejandro Frangi, Sheffield, UK
Chandrajit Bajaj, Austin, USA
Eugenio Oñate, Barcelona, Spain
Francisco Perales, Palma de Mallorca, Spain
G. A. Holzapfel, Graz University of Technology, Austria
J. Paulo Vilas-Boas, Porto, Portugal
Jeffrey A. Weiss, Salt Lake City, USA
John Middleton, Cardiff, UK
Jose M. García Aznar, Zaragoza, Spain
Perumal Nithiarasu, Swansea, UK
Kumar K. Tamma, Minneapolis, USA
Laurent Cohen, Paris, France
Manuel Doblaré, Zaragoza, Spain
Patrick J. Prendergast, Dublin, Ireland
Rainald Löhner, Fairfax, USA
Roger Kamm, Cambridge, USA
Shuo Li, London, Canada
Thomas J. R. Hughes, Austin, USA
Yongjie Zhang, Pittsburgh, USA
Theresearchrelatedtotheanalysisoflivingstructures(Biomechanics)hasbeenasourceof
recent research in several distinct areas of science, for example, Mathematics, Mechanical
Engineering, Physics, Informatics, Medicine and Sport. However, for its successful
achievement, numerous research topics should be considered, such as image processing
and analysis, geometric and numerical modelling, biomechanics, experimental analysis,
mechanobiology and enhanced visualization, and their application to real cases must be
developedandmoreinvestigationisneeded.Additionally,enhancedhardwaresolutionsand
less invasivedevices aredemanded.
Ontheotherhand,Image Analysis (ComputationalVision) isusedforthe extractionof
high level information from static images or dynamic image sequences. Examples of
applications involving image analysis can be the study of motion of structures from image
sequences,shapereconstructionfromimages,andmedicaldiagnosis.Asamultidisciplinary
area,ComputationalVisionconsiderstechniquesandmethodsfromotherdisciplines,suchas
ArtificialIntelligence,SignalProcessing,Mathematics,PhysicsandInformatics.Despitethe
many research projects in this area, more robust and efficient methods of Computational
ImagingarestilldemandedinmanyapplicationdomainsinMedicine,andtheirvalidationin
real scenarios is matterof urgency.
ThesetwoimportantandpredominantbranchesofScienceareincreasinglyconsideredtobe
stronglyconnectedandrelated.Hence,themaingoaloftheLNCV&Bbookseriesconsists
oftheprovisionofacomprehensiveforumfordiscussiononthecurrentstate-of-the-artinthese
fieldsbyemphasizingtheirconnection.Thebookseriescovers(butisnotlimitedto):
(cid:129) (cid:129)
ApplicationsofComputationalVisionand GridandHighPerformanceComputingfor
Biomechanics ComputationalVisionandBiomechanics
(cid:129) (cid:129)
BiometricsandBiomedicalPatternAnalysis Image-basedGeometricModelingandMesh
(cid:129) CellularImagingandCellularMechanics Generation
(cid:129) ClinicalBiomechanics (cid:129) ImageProcessingandAnalysis
(cid:129) ComputationalBioimagingandVisualization (cid:129) ImageProcessingandVisualizationin
(cid:129) Biofluids
ComputationalBiologyinBiomedicalImaging
(cid:129)
(cid:129) ImageUnderstanding
DevelopmentofBiomechanicalDevices
(cid:129)
(cid:129) MaterialModels
DeviceandTechniqueDevelopmentfor
(cid:129)
Mechanobiology
BiomedicalImaging
(cid:129) DigitalGeometryAlgorithmsforComputa- (cid:129) MedicalImageAnalysis
(cid:129)
tionalVisionandVisualization MolecularMechanics
(cid:129) (cid:129)
ExperimentalBiomechanics Multi-ModalImageSystems
(cid:129) (cid:129)
Gait&PostureMechanics MultiscaleBiosensorsinBiomedicalImaging
(cid:129) (cid:129)
MultiscaleAnalysisinBiomechanics MultiscaleDevicesandBiomems
(cid:129) NeuromuscularBiomechanics forBiomedicalImaging
(cid:129) NumericalMethodsforLivingTissues (cid:129) MusculoskeletalBiomechanics
(cid:129) NumericalSimulation (cid:129) SportBiomechanics
(cid:129) Software Development on Computational (cid:129) VirtualRealityinBiomechanics
(cid:129)
VisionandBiomechanics VisionSystems
More information about this series at http://www.springer.com/series/8910
J. Dinesh Peter Steven Lawrence Fernandes
(cid:129)
Carlos Eduardo Thomaz Serestina Viriri
(cid:129)
Editors
Computer Aided Intervention
and Diagnostics in Clinical
and Medical Images
123
Editors
J.Dinesh Peter Carlos Eduardo Thomaz
Department ofComputer Brazilian Computer Society
Science andEngineering Aalcides PlatinyAlves Batista
Karunya Institute of Technology Porto Alegre, RioGrande doSul,Brazil
andSciences
Coimbatore, Tamil Nadu,India Serestina Viriri
Schoolof Computer Science
StevenLawrence Fernandes University of KwaZulu-Natal
Department ofElectrical Durban,SouthAfrica
andComputer Engineering
University of Alabama atBirmingham
Birmingham, AL,USA
ISSN 2212-9391 ISSN 2212-9413 (electronic)
Lecture Notesin Computational Vision andBiomechanics
ISBN978-3-030-04060-4 ISBN978-3-030-04061-1 (eBook)
https://doi.org/10.1007/978-3-030-04061-1
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Contents
Brain Tissue Entropy Changes in Patients with Autism Spectrum
Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Sudhakar Tummala
Differential Coding-Based Medical Image Compression. . . . . . . . . . . . . 11
P. Chitra and M. Mary Shanthi Rani
Harmonization of White and Gray Matter Features in Diffusion
Microarchitecture for Cross-Sectional Studies . . . . . . . . . . . . . . . . . . . . 21
Prasanna Parvathaneni, Shunxing Bao, Allison Hainline, Yuankai Huo,
Kurt G. Schilling, Hakmook Kang, Owen Williams, Neil D. Woodward,
Susan M. Resnick, David H. Zald, Ilwoo Lyu and Bennett A. Landman
Deep Neural Architecture for Localization and Tracking of Surgical
Tools in Cataract Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Neha Banerjee, Rachana Sathish and Debdoot Sheet
Efficient Segmentation of Medical Images Using Dilated Residual
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Lokeswara Rao Bonta and N. Uday Kiran
Non-rigid Registration of Brain MR Images for Image Guided
Neurosurgery Using Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . 49
D. Preetha Evangeline and P. Anandhakumar
AHybridFusionofMultimodalMedicalImagesfortheEnhancement
of Visual Quality in Medical Diagnosis. . . . . . . . . . . . . . . . . . . . . . . . . . 61
S. Sandhya, M. Senthil Kumar and L. Karthikeyan
An Amplifying Image Approach: Non-iterative Multi Coverage
Image Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
K. Elaiyaraja, M. Senthil Kumar and L. Karthikeyan
v
vi Contents
U-Net Based Segmentation and Multiple Feature Extraction
of Dermascopic Images for Efficient Diagnosis of Melanoma . . . . . . . . . 81
D. Roja Ramani and S. Siva Ranjani
Secured Transmission of Medical Images in Radiology
Using AES Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Pavithra Prabhu and K. N. Manjunath
A Review on Haze Removal Techniques . . . . . . . . . . . . . . . . . . . . . . . . 113
K. P. Senthilkumar and P. Sivakumar
Secured Image Transmission in Medical Imaging
Applications—A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Pavithra Prabhu and K. N. Manjunath
Evolution of Methods for NGS Short Read Alignment and Analysis
of the NGS Sequences for Medical Applications. . . . . . . . . . . . . . . . . . . 135
J. A. M. Rexie and Kumudha Raimond
Caries Detection in Non-standardized Periapical Dental X-Rays . . . . . . 143
D. Osterloh and Serestina Viriri
Segmentation of Type II Diabetic Patient’s Retinal Blood Vessel
to Diagnose Diabetic Retinopathy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
T. Jemima Jebaseeli, C. Anand Deva Durai and J. Dinesh Peter
A Novel Corner Elimination Method for the Compression
of Wireless Capsule Endoscopic Videos . . . . . . . . . . . . . . . . . . . . . . . . . 161
Caren Babu and D. Abraham Chandy
Prediction of Two Year Survival Among Patients of Non-small
Cell Lung Cancer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Yash Dagli, Saumya Choksi and Sudipta Roy
Prediction of Chronic Kidney Diseases Using Deep Artificial
Neural Network Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Himanshu Kriplani, Bhumi Patel and Sudipta Roy
Monitoring Acute Lymphoblastic Leukemia Therapy with Stacked
Denoising Autoencoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
Jakob Scheithe, Roxane Licandro, Paolo Rota, Michael Reiter,
Markus Diem and Martin Kampel
Modified Low-Power Built-in Self-test for Image Processing
Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
P. Anitha, P. Ramanathan and P. T. Vanathi
A Hassle-Free Shopping Experience for the Visually Impaired:
An Assistive Technology Application . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Sherin Tresa Paul and Kumudha Raimond
Contents vii
Retina as a Biomarker of Stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
R. S. Jeena, A. Sukeshkumar and K. Mahadevan
Distributed Representation of Healthcare Text Through Qualitative
and Quantitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
J. R. Naveen, H. B. Barathi Ganesh, M. Anand Kumar and K. P. Soman
Detection of Lymph Nodes Using Centre of Mass and Moment
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
R. Akshai, S. Rohit Krishnan, G. Swetha and B. P. Venkatesh
Estimation of Elbow Joint Angle from Surface Electromyogram
Signals Using ANFIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
P. Rajalakshmy, Elizabeth Jacob and T. Joclyn Sharon
Video Stabilization for High-Quality Medical Video Compression . . . . . 255
D. Raveena Judie Dolly, D. J. Jagannath and J. Dinesh Peter
Significance of Global Vectors Representation in Protein
Sequences Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Anon George, H. B. Barathi Ganesh, M. Anand Kumar and K. P. Soman
TextureAnalysisonThyroidUltrasoundImagesfortheClassification
of Hashimoto Thyroiditis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
S. Kohila and G. Sankara Malliga
Cluster Based Paddy Leaf Disease Detection, Classification
and Diagnosis in Crop Health Monitoring Unit . . . . . . . . . . . . . . . . . . . 281
A. D. Nidhis, Chandrapati Naga Venkata Pardhu, K. Charishma Reddy
and K. Deepa
Detection of Glaucoma Using Anterior Segment Optical Coherence
Tomography Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
P. Priyanka, V. Norris Juliet and S. Shenbaga Devi
Brain Tissue Entropy Changes in Patients
with Autism Spectrum Disorder
SudhakarTummala
Abstract AutismSpectrumDisorder(ASD)isaccompaniedbybraintissuechanges
inareasthatcontrolbehavior,cognition,andmotorfunctions,deficientinthedisor-
der.TheobjectiveofthisresearchwastoevaluatebrainstructuralchangesinASD
patientscomparedtocontrolsubjectsusingvoxel-by-voxelimageentropyfromT1-
weighted imaging data of 115 ASD and 105 control subjects from autism brain
imaging data exchange. For all subjects, entropy maps were calculated, normal-
izedtoacommonspaceandsmoothed.Then,theentropymapswerecomparedat
each voxel between groups using analysis of covariance (covariates; age, gender).
IncreasedentropyinASDpatients,indicatingchronicinjury,emergedinseveralvital
regionsincludingfrontaltemporalandparietalloberegions,corpuscallosum,cingu-
latecortices,andhippocampi.Entropyprocedureshowedsignificanteffectsizeand
demonstrated wide-spread changes in sites that control social behavior, cognitive,
andmotoractivities,suggestingseveredamageinthoseareas.Theneuropathological
mechanismscontributingtotissueinjuryremainunclearandpossiblyduetofactors
includinggenetic,atypicalearlybraingrowthduringchildhood.
· ·
Keywords Magneticresonanceimaging Entropy Autismspectrumdisorder
1 Introduction
Autism spectrum disorder (ASD) is a diverse group of neurodevelopmental disor-
dersrepresentedbyrepetitivebehaviors,abnormalsocialinteraction,anddiminished
cognition[1,2].Subjectswithlong-standingASDshowregionalandvoxel-leveltis-
suechangesinseveralbrainsitesthatregulatemotor,social-cognitive,andmood,as
evaluatedwithhigh-resolutionT1-weighted-imaging-basedvoxel-basedmorphom-
etry(VBM)anddiffusiontensorimaging(DTI)-baseddiffusivityproceduresfrom
B
S.Tummala( )
DepartmentofElectronicsandCommunicationEngineering,
SRMUniversity-AP,Amaravati522503,AndhraPradesh,India
e-mail:[email protected]
©SpringerNatureSwitzerlandAG2019 1
J.D.Peteretal.(eds.),ComputerAidedInterventionandDiagnosticsinClinical
andMedicalImages,LectureNotesinComputationalVisionandBiomechanics31,
https://doi.org/10.1007/978-3-030-04061-1_1
2 S.Tummala
magnetic resonance imaging (MRI) [3–6]. VBM procedure has limited sensitivity
from the inherent limited range of probability values, and is not suitable to detect
subtlechronic/acutegrayorwhitematterchanges;thus,theprocedureisunableto
differentiateacutefromchronictissuepathology.Although,DTIbasedmeandiffu-
sivity,axialandradialdiffusivitymetrics,candifferentiatesuchacuteversuschronic
differences, those images also have an inferior spatial resolution, and require spe-
cializedpreprocessing.
Imagetextureisameasurethatquantifiesspatialpatternsofintensities/graylevel
valuesandthesespatialpatternsmaydifferwithrespecttothenatureanddegreeof
tissueinjury.Forthispurpose,T1-weightedimageswereemployed,whicharebetter
suitedtodetectthechangesinintensitypatternsduetohigherspatialresolutionand
bettergrayandwhitemattercontrast.
Entropy,atexturefeature,measurestheextentofhomogeneityorrandomnessina
givenregion,basedoncharacteristicsoftheintensityhistogramfromhigh-resolution
structural images. The entropy values are inversely proportional to the amount of
water content in the tissue. In acute stages of the ASD disease, due to axonal and
neuralswelling,theamountofwatercontentincreasesandentropyvaluesdecreases,
whereasinchronicstagesofthedisease,theamountofwatercontentdecreasesand
entropy values will be increased. The entropy technique has been used to assess
the neural changes in different conditions, including Alzheimer’s [7], Parkinson’s
disease [8], characterization of intracranial tumors [9], and acute inflammation in
MSlesions[10].There werefewregion-based studies relatedtoimage texturefor
assessingbraintissueinASDsubjects[11,12].However,tomyknowledgeapplying
voxel-levelentropyprocedureonalargerpopulationtostudybraintissuechangesin
ASDpatientsisnovel.
Here, the aim was to investigate regional brain entropy changes in ASD sub-
jects using high-resolution T1-weighted images from Autism Brain Imaging Data
Exchange(ABIDEI)database.Thehypothesisisthatvoxel-levelentropyvaluesare
higherinASDsubjectsinvariousbrainsitesinvolvedincognitive,motorandneu-
ropsychologicregulationcomparedtocontrolsubjects,indicativeofchronictissue
changesinthoseregulatorysites.
2 MaterialsandMethods
2.1 StudyPopulation
FromABIDEIdatabase,115subjectswithASDand105age-andgender-comparable
controlsubjectswereselected.Thecontrolsubjectswererecruitedthroughseveral
participatingsitesacrosstheUSA.SubjectswithASDwereincludedbasedonclin-
icians Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Test