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Computer vision approach to classification of circulating tumor cells

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dc.contributor.authorHopkins, David, author
dc.contributor.authorKirby, Michael, advisor
dc.contributor.authorPeterson, Chris, committee member
dc.contributor.authorGivens, Geof, committee member
dc.date.accessioned2007-01-03T05:01:51Z
dc.date.available2007-01-03T05:01:51Z
dc.date.issued2013
dc.description.abstractCurrent research into the detection and characterization of circulating tumor cells (CTCs) in the bloodstream can be used to assess the threat to a potential cancer victim. We have determined specific goals to further the understanding of these cells. 1) Full automation of an algorithm to overcome the current methods challenges of being labor-intensive and time-consuming, 2) Detection of single CTC cells amongst several million white blood cells given digital imagery of a panel of blood, and 3) Objective classification of white blood cells, CTCs, and potential sub-types. We demonstrate in this paper the developed theory, code and implementation necessary for addressing these goals using mathematics and computer vision techniques. These include: 1) Formation of a completely data-driven methodology, and 2) Use of Bag of Features computer vision technique coupled with custom-built pixel-centric feature descriptors, 3) Use of clustering techniques such as K-means and Hierarchical clustering as a robust classification method to glean insights into cell characteristics. To objectively determine the adequacy of our approach, we test our algorithm against three benchmarks: sensitivity/specificity in classification, nontrivial event detection, and rotational invariance. The algorithm performed well with the first two, and we provide possible modifications to improve performance on the third. The results of the sensitivity and specificity benchmark are important. The unfiltered data we used to test our algorithm were images of blood panels containing 44,914 WBCs and 39 CTCs. The algorithm classified 67.5 percent of CTCs into an outlier cluster containing only 300 cells. A simple modification brought the classification rate up to 80 percent of total CTCs. This modification brings the cluster count to only 400 cells. This is a significant reduction in cells a pathologist would sort through as it is only .9 percent of the total data.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierHopkins_colostate_0053N_11712.pdf
dc.identifier.urihttp://hdl.handle.net/10217/79065
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectbag of features
dc.subjectcancer
dc.subjectcell classification
dc.subjectcirculating tumor cells
dc.subjectcomputer vision
dc.subjectfeature descriptor
dc.titleComputer vision approach to classification of circulating tumor cells
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineMathematics
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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