Computer Aided Intervention and Diagnostics in Clinical and Medical Images PDF
Preview Computer Aided Intervention and Diagnostics in Clinical and Medical Images
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 LibraryofCongressControlNumber:2018961232 ©SpringerNatureSwitzerlandAG2019 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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
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