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A computer vision approach to classi...

Hopkins, David.

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A computer vision approach to classification of circulating tumor cells.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A computer vision approach to classification of circulating tumor cells./
Author:	Hopkins, David.
Description:	93 p.
Notes:	Source: Masters Abstracts International, Volume: 52-01.
Contained By:	Masters Abstracts International52-01(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1539638
ISBN:	9781303153655

A computer vision approach to classification of circulating tumor cells.
Hopkins, David.

A computer vision approach to classification of circulating tumor cells. - 93 p.

Source: Masters Abstracts International, Volume: 52-01.

Thesis (M.S.)--Colorado State University, 2013.

Current 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.

ISBN: 9781303153655Subjects--Topical Terms:

515831
Mathematics.

A computer vision approach to classification of circulating tumor cells.
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020 $a 9781303153655
035 $a (MiAaPQ)AAI1539638
035 $a AAI1539638
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hopkins, David. $3 640590
245 1 2 $a A computer vision approach to classification of circulating tumor cells.
300 $a 93 p.
500 $a Source: Masters Abstracts International, Volume: 52-01.
500 $a Adviser: Michael Kirby.
502 $a Thesis (M.S.)--Colorado State University, 2013.
520 $a Current 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.
520 $a 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.
520 $a 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.
590 $a School code: 0053.
650 4 $a Mathematics. $3 515831
650 4 $a Computer Science. $3 626642
650 4 $a Health Sciences, Oncology. $3 1018566
690 $a 0405
690 $a 0984
690 $a 0992
710 2 $a Colorado State University. $b Mathematics. $3 1683531
773 0 $t Masters Abstracts International $g 52-01(E).
790 $a 0053
791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1539638