Advanced Computational Approaches to Biomedical Engineering PDF
Preview Advanced Computational Approaches to Biomedical Engineering
Punam K. Saha · Ujjwal Maulik Subhadip Basu E ditors Advanced Computational Approaches to Biomedical Engineering Advanced Computational Approaches to Biomedical Engineering Punam K. Saha • Ujjwal Maulik (cid:129) Subhadip Basu Editors Advanced Computational Approaches to Biomedical Engineering 123 Editors PunamK.Saha UjjwalMaulik Dept.ofElectricalandComputer SubhadipBasu Engineering Dept.ofComputerScienceandEngineering UniversityofIowa JadavpurUniversity IowaCity,IA,USA Kolkata,India ISBN978-3-642-41538-8 ISBN978-3-642-41539-5(eBook) DOI10.1007/978-3-642-41539-5 SpringerHeidelbergNewYorkDordrechtLondon LibraryofCongressControlNumber:2014930169 ©Springer-VerlagBerlinHeidelberg2014 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerptsinconnection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. 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Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) To mylategrandmotherManoramaSaha PunamKumar Saha To SanghamitraandUtsav Ujjwal Maulik To myparentsShyamalKumar BasuandRita Basu,mywifeKoel, andmydaughterAishiki SubhadipBasu Preface Over the last few decades, advancements in biomedical imaging, modeling, and sensing systems, coupled with rapid growth in computational and networking technologies, analytic approaches, visualization and virtual-reality, man–machine interaction,andautomation,haveledtogrowinginterestinbiomedicalengineering. While research interest in this field is rapidly emerging among international communities, most leading universities around the globe have already adopted academiccurriculaonbiomedicalengineeringandrelatedsubjects. In general, biomedical engineering relates to the application of engineering principlestoadvancethemedicalandbiologicalsciences.Itcomprisesseveraltop- ics, includingbiomedicine,medicalimaging,physiologicalmodelingandsensing, instrumentation,real-timesystems,microtechniques,automationandcontrol,signal processing, image reconstruction, visualization, processing and analysis, pattern recognitionanddatamining,andbiomechanics. Biomedical engineering holds great promise for advancing clinical research in medicine, pharmaceuticals, and public health services focusing on prevention, diagnosis, and treatment of complex medical conditions and understanding the etiologiesofdifferentbiologicalprocesses.Inrecentyears,biomedicalengineering has taken on a significant role in clinical research, facilitating the developmentof new drugs, innovative implants and prosthetics, novel imaging technologies, and improvedtoolsfordiagnosticandtherapeuticmechanisms. Thisbookbringstogethercontributionsfromleadingresearchgroupspresenting recentadvancesinvariousactivefieldsofbiomedicalengineering,includingdiffer- ent imaging technologies, image processing and visualization, pattern recognition and data mining, and their clinical applications. Major sources of biomedical imaging data include X-ray computed tomography, magnetic resonance imaging, ultrasound imaging, and positron emission tomography. Often, biomedical data are associatedwith severalsignificantchallengesincludinglimited signal-to-noise ratios and spatial and temporal resolution, misregistration of deformable data in cross-sectionalandlongitudinalimagingstudies,theinherentlycomplexandfuzzy nature of the target objects or information, and large image and data volumes. The overarchinggoal of most medicalimaging research programsis to overcome vii viii Preface these challengeswith a focusonaccurateandreproducibleinformationgathering, enhancedautomation at variouslevels of imaging and data analysis, and the inte- grationofmultidisciplinaryexpertisetowardfacilitatingdiagnosticandtherapeutic practices. The chapters in this book are divided into two parts: Part I includes chapters primarilydedicatedtocomputationalapproachestomedicalimaging,whilePartII consistsofchaptersdevotedto task-specificbiomedicalimagingapplications.The methodsandalgorithmspresentedinthisbookincludemedicalimagesegmentation, information-theoretic clustering, multiobjective differential-evolution-based fuzzy clustering, spectral and nonlinear analysis, and metalearning approaches. The secondpartofthisbookcoversanumberofbiomedicalapplicationsacrossvarious imaging modalities, including 3D ultrasound, fluorescent confocal microscopy, magneticresonanceimaging,andX-raycomputedtomography. Fivechaptersonadvancedcomputationalmethodsofbiomedicalengineeringare includedinthefirstpartofthisbook.Inthechapter“GraphAlgorithmicTechniques forBiomedicalImageSegmentation,”GarvinandWupresentanin-depthoverview oftwostate-of-the-artgraph-basedmethods,viz.,graphcutsaswellasthelayered optimalgraphimagesegmentationofmultipleobjectsandsurfaces(LOGISMOS) approach,for segmentingthree-dimensionalstructuresin medicalimages.In each case,anoverviewoftheunderlyingoptimizationproblemispresentedfirst,followed bythegraph-basedrepresentationoftheproblemwhichenablesthegloballyoptimal solutiontobefoundinpolynomialtime. In the chapter “Information Theoretic Clustering for Medical Image Segmen- tation,” Hill et al. review a recent approach to clustering under an information- theoretic framework that efficiently finds the suitable number of clusters in a medicalimagerepresentingdifferenttissuecharacteristics.Theproposedclustering approachoptimizesanobjectivefunctionquantifyingthequalityofagivencluster configuration. In the chapter “Multiobjective Differential Evolution Based Fuzzy Clustering for MR Brain Image Segmentation,”Saha and Maulik presenta multi- objective differential-evolution-basedfuzzy clustering technique for simultaneous optimizationofmultipleclusteringmeasures.Heredifferentialevolutionisusedas theunderlyingoptimizationtechniqueandclustercentersareencodedinvectorsin differentialevolution.ThetechniqueisevaluatedonseveralsimulatedT1-weighted, T2-weighted,andprotondensity-weightedmagneticresonance(MR)brainimages forclassifyingnormalandmultiplesclerosislesions. In the chapter “Spectral and Non-linear Analysis of Thalamocortical Neural Mass Model Oscillatory Dynamics,” Sen-Bhattacharya et al. first present the multimodal analytical techniques for investigating abnormal brain oscillations in Alzheimer’s disease. They focus on a combined power spectral and nonlinear behavioral analysis of a neural mass model of the thalamocortical circuitry. They also present the power spectral analysis on a model that implements feedforward andfeedbackconnectivitiesinathalamocorticothalamiccircuitry. In the chapter “A Meta-learning Approach for Protein Function Prediction,” Plewczynski and Basu present a review of multiscale protein biological function predictionalgorithms.Theyalso highlightthe advantagesof using a metalearning Preface ix approach for protein function prediction, particularly related to protein sequence analysis, 3D structure comparison, biological function annotation, and molecular interactionpredictions.Theyincludediversecomputationalmethodsforpredicting the biological function for a given biomolecule using multiscale features and a metalearning prediction system to analyze the impact of microdynamics on the globalbehaviorofselectedbiologicalsystems. The second part of the book contains four chapters on important biomedi- cal applications. In the chapter “Segmentation of the Carotid Arteries from 3D Ultrasound Images,” Ukwatta and Fenster present an accurate and robust method for segmentation of the carotid arteries from 3D ultrasound images allowing efficient quantification of carotid atherosclerosis. They describe the carotid vessel segmentationalgorithmanddemonstratethat3Dultrasoundisaviabletechniquefor quantifyingtheprogressionandregressionofcarotidatherosclerosis.Inthechapter “Contemporary Problems in Quantitative Image Analysis in Structural Neuronal Plasticity,” Ruszczycki et al. study the brain structure at the microscopic level to elucidate the processes of structural neuronal plasticity to understand various neurodegenerativediseases.Theyusedfluorescentconfocalmicroscopyimagesfor quantitativeanalysisofbraintissues. In the chapter “Advanced MRI of Cartilage and Subchondral Bone in Osteoarthritis,” Chang and Regatte provide an overview of MR imaging techniquestoevaluatecartilageandsubchondralbonemicrostructure,andcartilage biochemical composition, in order to provide a noninvasive alternative to detect pathologicalalterationsinthesetwotissuesforearlydiagnosisofosteoarthritisinat- risksubjects. Thederivedknowledgeandcapabilitiesshouldultimatelyaccelerate the discovery and testing of novel therapies to treat osteoarthritis, a disease that representsanenormoussocioeconomicandhealthburdenonsociety. In the final chapter of the book, Chowdhury et al. address the problem of detectionofhairlinemandibularfracturesfromcomputedtomography(CT)images. Ithas beenobservedthat suchfracturesare difficultto detectmanuallydue to the absence of sharp surface and contourdiscontinuitiesand the presenceof intensity inhomogeneity in the CT images. In the presented work, a two-stage method is adoptedtofirstidentifythe2DCTimageslicesofamandiblewithhairlinefractures from a fractured craniofacial skeleton. In the second stage, the hairline fracture is detected using a maximum-flow minimum-cut algorithm from the previously identifiedsubsetofimages. Overall, the book chapters cover a wide spectrum of biomedical engineering research related to advanced computational methods and their applications. Cur- rently there are not many books available that address these important research discipline in one volume. This edited book, which is unique in its character, will be useful to graduate students and researchers in computer science, biomedical engineering, computational and molecular biology, electrical engineering, system science,andinformationtechnology,bothasa textandareferencebookforsome parts of the curriculum. Researchers and practitioners in industrial and research laboratorieswillalsobenefit.
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