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Quantitative analysis of digitized h...

Mosquera Lopez, Clara Marcela.

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Quantitative analysis of digitized histopathology images with applications to prostate cancer screening.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantitative analysis of digitized histopathology images with applications to prostate cancer screening./
Author:	Mosquera Lopez, Clara Marcela.
Description:	163 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:	Dissertation Abstracts International76-09B(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3702345
ISBN:	9781321736014

Quantitative analysis of digitized histopathology images with applications to prostate cancer screening.
Mosquera Lopez, Clara Marcela.

Quantitative analysis of digitized histopathology images with applications to prostate cancer screening. - 163 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2015.

This item is not available from ProQuest Dissertations & Theses.

Advances in digital pathology technology have made possible the development of computer-aided diagnosis (CAD) systems based on quantitative analysis of high-content histopathology images. CAD systems have the potential to produce fast, objective, and consistent diagnosis outcomes to support pathologists in diagnosing several diseases in daily practice. The goal of this dissertation is the development of a framework for automated detection and grading of prostate cancer (PCa) regions from digitized histopathology based on the Gleason grading system. The main contributions of this work are fivefold. First, we present a comprehensive literature review of state-of-the-art systems and methods for quantitative analysis of digitized prostate histopathology using texture and morphology tissue descriptors. Second, we propose a fuzzy color standardization method that reduces the color variations observed in digitized histopathology images by locally transferring color statistics from a reference image to a target image without significantly altering the structure of the standardized image. Color standardization is a preprocessing step that allows us to analyze images from different sources. Third, we develop a system for automated classification of preselected cancerous regions belonging to Gleason grades 3, 4, and 5. This system assembles a set of binary classifiers trained on newly-developed tissue descriptors including color and wavelet-based features for robust cancer grading. Fourth, we propose a framework for automated detection and grading of cancerous regions from digitized tissue microarray cores and whole-slide images based on a combination of morphology and texture features. Finally, we statistically analyze the impact of using the proposed decision support tools on the percentage of agreement between pathologists in the assignment of Gleason grades to tissue samples.

ISBN: 9781321736014Subjects--Topical Terms:

649834
Electrical engineering.

Quantitative analysis of digitized histopathology images with applications to prostate cancer screening.
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500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
500 $a Adviser: Sos S. Agaian.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Advances in digital pathology technology have made possible the development of computer-aided diagnosis (CAD) systems based on quantitative analysis of high-content histopathology images. CAD systems have the potential to produce fast, objective, and consistent diagnosis outcomes to support pathologists in diagnosing several diseases in daily practice. The goal of this dissertation is the development of a framework for automated detection and grading of prostate cancer (PCa) regions from digitized histopathology based on the Gleason grading system. The main contributions of this work are fivefold. First, we present a comprehensive literature review of state-of-the-art systems and methods for quantitative analysis of digitized prostate histopathology using texture and morphology tissue descriptors. Second, we propose a fuzzy color standardization method that reduces the color variations observed in digitized histopathology images by locally transferring color statistics from a reference image to a target image without significantly altering the structure of the standardized image. Color standardization is a preprocessing step that allows us to analyze images from different sources. Third, we develop a system for automated classification of preselected cancerous regions belonging to Gleason grades 3, 4, and 5. This system assembles a set of binary classifiers trained on newly-developed tissue descriptors including color and wavelet-based features for robust cancer grading. Fourth, we propose a framework for automated detection and grading of cancerous regions from digitized tissue microarray cores and whole-slide images based on a combination of morphology and texture features. Finally, we statistically analyze the impact of using the proposed decision support tools on the percentage of agreement between pathologists in the assignment of Gleason grades to tissue samples.
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