Cell Phenotype Analyzer: Automated Techniques for Cell Phenotyping using Contactless Dielectrophoresis
| dc.contributor.author | Bala, Divya Chandrakant | en |
| dc.contributor.committeechair | Abbott, A. Lynn | en |
| dc.contributor.committeemember | Parikh, Devi | en |
| dc.contributor.committeemember | Schmelz, Eva M. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2016-06-24T08:01:16Z | en |
| dc.date.available | 2016-06-24T08:01:16Z | en |
| dc.date.issued | 2016-06-23 | en |
| dc.description.abstract | Cancer 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 |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:8375 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/71428 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Cell Phenotype | en |
| dc.subject | Computer Vision | en |
| dc.subject | Dielectrophoresis | en |
| dc.title | Cell Phenotype Analyzer: Automated Techniques for Cell Phenotyping using Contactless Dielectrophoresis | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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