Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/60567
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dc.contributor.authorMerlin Veronika Arokiamary James
dc.date.accessioned2014-05-28T07:20:18Z
dc.date.available2014-05-28T07:20:18Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10356/60567
dc.description.abstractSubpopulation heterogeneity has been spawning intense studies at genetic and molecular level due to its occurrence at all biological levels from cells to tissues. We envisioned studying this biological phenomenon through image based profiling methods incorporating motility based features. We developed population profiling methods for analysing subpopulations arising in single-cell lines by introducing motility based dynamic features. Combination of these features with morphological features improved the accuracy of classification of cell states. We introduced unsupervised methods so that prior training data is not required. Also the use of motility features for identifying membrane dynamics and its correlation with whole cell dynamics were investigated. We were able to identify subpopulations of cells with similar dynamic profiles but having different membrane patterns. The profiling pipeline using dynamic features were demonstrated by identifying mitotic phases in cells undergoing cell-cycle. Cells passing through mitotic division exhibit motility characteristics unique to each phase which were utilized for phase recognition. The methods were validated with real image data and the results compared well with ground truth.en_US
dc.format.extent156 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleCell profiling with dynamic features for high-throughput imagesen_US
dc.typeThesis
dc.contributor.supervisorRajapakse Jagath Chandanaen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeDoctor of Philosophy (SCE)en_US
dc.contributor.researchSingapore-MIT Alliance Programmeen_US
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