Geometric Multiscale Analysis and Applications to Neuroscience Imaging

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dc.contributor.advisorLabate, Demetrio
dc.contributor.committeeMemberLaezza, Fernanda
dc.contributor.committeeMemberJosić, Krešimir
dc.contributor.committeeMemberPapadakis, Emanuel I.
dc.creatorKayasandik, Cihan Bilge 1989-
dc.creator.orcid0000-0002-9282-6568
dc.date.accessioned2019-09-18T01:29:56Z
dc.date.available2019-09-18T01:29:56Z
dc.date.createdAugust 2017
dc.date.issued2017-08
dc.date.submittedAugust 2017
dc.date.updated2019-09-18T01:29:57Z
dc.description.abstractThis thesis is concerned with the development of quantitative methods for the analysis of neuronal images. Automated detection and segmentation of components of neurons in fluorescent images is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where one needs to compute multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection and neurite tracing still have significant limitations which are more severe when processing fluorescence image stacks of neuronal cultures. To address such challenges, in this dissertation, we develop several novel methods and algorithms aimed at extracting quantitative information in fluorescent images of neuronal cultures or brain tissue, including methods for the automated detection of the soma and other subcellullar structures of interest, and algorithms for cell classification. Our methods rely on technique from harmonic analysis, especially wavelets and more advanced multiscale representation systems. Using these techniques, we are able to extract highly informative image characteristics with high geometric sensitivity and computational efficiency. As part of our work, we include a theoretical justification and an extensive numerical validation on microscopy imaging data provided by our collaborators in neuriscience. An extensive comparison with state-of-the-art existing methods demonstrate that our algorithms are highly competitive in terms of accuracy, reliability and computational efficiency.
dc.description.departmentMathematics, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Kayasandik, Cihan Bilge, and Demetrio Labate. "Improved detection of soma location and morphology in fluorescence microscopy images of neurons." Journal of neuroscience methods 274 (2016): 61-70.
dc.identifier.urihttps://hdl.handle.net/10657/4795
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectConfocal microscopy
dc.subjectFluorescence
dc.subjectMicroscopy
dc.subjectImage analysis
dc.subjectMultiscale analysis
dc.subjectNeuronal morphology
dc.subjectSoma detection
dc.subjectAutomated extraction of neuronal trees
dc.titleGeometric Multiscale Analysis and Applications to Neuroscience Imaging
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentMathematics, Department of
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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