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Automated Detection of Circulating C...

Emerson, Tegan Halley.

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Automated Detection of Circulating Cells Using Low Level Features.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automated Detection of Circulating Cells Using Low Level Features./
Author:	Emerson, Tegan Halley.
Description:	68 p.
Notes:	Source: Masters Abstracts International, Volume: 52-02.
Contained By:	Masters Abstracts International52-02(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1544756
ISBN:	9781303360824

Automated Detection of Circulating Cells Using Low Level Features.
Emerson, Tegan Halley.

Automated Detection of Circulating Cells Using Low Level Features. - 68 p.

Source: Masters Abstracts International, Volume: 52-02.

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

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.

ISBN: 9781303360824Subjects--Topical Terms:

515831
Mathematics.

Automated Detection of Circulating Cells Using Low Level Features.
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100 1 $a Emerson, Tegan Halley. $3 2101123
245 1 0 $a Automated Detection of Circulating Cells Using Low Level Features.
300 $a 68 p.
500 $a Source: Masters Abstracts International, Volume: 52-02.
500 $a Adviser: Michael Kirby.
502 $a Thesis (M.S.)--Colorado State University, 2013.
520 $a 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.
590 $a School code: 0053.
650 4 $a Mathematics. $3 515831
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0405
690 $a 0984
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710 2 $a Colorado State University. $b Mathematics. $3 1683531
773 0 $t Masters Abstracts International $g 52-02(E).
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791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1544756