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Automated detection of circulating cells using low level features




Emerson, Tegan Halley, author
Kirby, Michael, advisor
Peterson, Chris, committee member
Nyborg, Jennifer, committee member

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This thesis addresses the problem of detection of high definition circulating tumor cells using data driven feature selection. We propose techniques in pattern analysis and computer vision to achieve this goal. Specifically, we determine a set of low level features which can structurally differentiate between different cell types of interest to contribute to the treatment and monitoring of patients. We have implemented three image representation techniques on a curated data set. The curated data set consists of digitized images of 1000 single cells: 500 of which are high definition circulating tumor cells or other cells of high interest, and 500 of which are white blood cells. None of the three image representation techniques have been previously applied to this data set. One image representation is a novel contribution and is based on the characterization of a cell in terms of its concentric Fourier rings. The Fourier Ring Descriptors (FRDs) exploit the size variations and morphological differences between events of high and low interest while being rotationally invariant. Using the low level descriptors, FRDs, as a representation with a linear support vector machine decision tree classifier we have been able to average 99.34% accuracy on the curated data set and 99.53% on non-curated data. FRDs exhibit robustness to rotation and segmentation error. We discuss the applications of the results to clinical use in context of data provided by The Kuhn Laboratory at The Scripps Research Institute.


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