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dc.contributor.advisorPalaniappan, Kannappaneng
dc.contributor.authorErsoy, Ilkereng
dc.date.issued2014eng
dc.date.submitted2014 Summereng
dc.descriptionAbstract from public.pdf.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Advances in automated digital microscopy imaging made it possible to produce multi-dimensional image data that can capture dynamic characteristics of sub-cellular and cellular structures. Biologists routinely produce large volumes of bioimage time lapse data that necessitates automated algorithms for unbiased and repeatable quantitative analysis. These algorithms are the stepping stones in bioimage informatics to turn the image data into biological knowledge. Unique challenges posed by different imaging modalities and cell dynamics require a combination of accurate detection, segmentation, classification and tracking approaches tailored to address and exploit particular image characteristics. In this dissertation, we present algorithms for the analysis of microscopy image sequences to address these challenges. We propose a level set active contour approach to address accurate segmentation in phase-contrast as well as brightfield microscopy imaging that utilizes edge profiles. Our approach significantly outperforms traditional level set approaches. We show the applications of our approach to cell spreading analysis and red blood cell analysis with robust solutions for cell detection to delineate clustered cells. We also present two studies for automated classification of cells in fluorescence microscopy emphasizing the importance of choosing image features for the specific problem. Lastly, we present a fully automated cell detection and tracking approach tailored for muscle satellite cells that enables efficient and unbiased analysis of factors that promote cell motility.eng
dc.identifier.urihttps://hdl.handle.net/10355/46443
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertations.eng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campuses of the University of Missouri.eng
dc.sourceSubmitted by the University of Missouri--Columbia Graduate School.eng
dc.subject.FASTImaging systems in biologyeng
dc.subject.FASTCells -- Analysiseng
dc.subject.FASTAutomatic classificationeng
dc.titleImage and video analysis techniques for cellular microscopyeng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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