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dc.contributor.authorBala, Divya Chandrakanten_US
dc.date.accessioned2016-06-24T08:01:16Z
dc.date.available2016-06-24T08:01:16Z
dc.date.issued2016-06-23en_US
dc.identifier.othervt_gsexam:8375en_US
dc.identifier.urihttp://hdl.handle.net/10919/71428
dc.description.abstractCancer is among the leading causes of death worldwide. In 2012, there were 14 million new cases and 8.2 million cancer-related deaths worldwide. The number of new cancer cases is expected rise to 22 million within the next two decades. Most chronic cancers cannot be cured. However, if the precise cancer cell type is diagnosed at an earlier, less aggressive stage then the chance of curing the disease increases with accurate drug delivery. This work is a humble contribution to the advancement of cancer research. This work delves into biological cell phenotyping under a dielectrophoresis setup using computer vision. Dielectrophoresis is a well-known phenomenon in which dielectric particles are subjected to a non-homogeneous electric field. This work is an analytical part of a larger proposed system replete with hardware, software and microfluidics integration to achieve cancer cell characterization, separation and enrichment using contactless dielectrophoresis. To analyze the cell morphology, various detection and tracking algorithms have been implemented and tested on a diverse dataset comprising cell-separation video sequences. Other related applications like cell-counting and cell-proximity detection have also been implemented. Performances were evaluated against ground truth using metrics like precision, recall and RMS cell-count error. A detection approach using difference of Gaussian and super-pixel algorithm gave the highest average F-measure of 0.745. A nearest neighbor tracker and Kalman tracking method gave the best overall tracking performance with an average F-measure of 0.95. This combination of detection and tracking methods proved to be best suited for this dataset. A graphical user interface to automate the experimentation process of the proposed system was also designed.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectCell Phenotypeen_US
dc.subjectComputer Visionen_US
dc.subjectDielectrophoresisen_US
dc.titleCell Phenotype Analyzer: Automated Techniques for Cell Phenotyping using Contactless Dielectrophoresisen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairAbbott, Amos L.en_US
dc.contributor.committeememberParikh, Devien_US
dc.contributor.committeememberSchmelz, Eva M.en_US


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