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Classification in thoracic computed ...

Kim, Hyun Jung.

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Classification in thoracic computed tomography image data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Classification in thoracic computed tomography image data./
Author:	Kim, Hyun Jung.
Description:	146 p.
Notes:	Adviser: Gang Li.
Contained By:	Dissertation Abstracts International68-07B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3272275
ISBN:	9780549130444

Classification in thoracic computed tomography image data.
Kim, Hyun Jung.

Classification in thoracic computed tomography image data. - 146 p.

Adviser: Gang Li.

Thesis (Ph.D.)--University of California, Los Angeles, 2007.

A challenge for computed tomography (CT) image analysis is to produce quantitative information of parenchymal abnormalities generalizable to a population of interest. Typically CT data from patient populations are heterogeneous, spatially correlated, massive and high dimensional. In order to classify abnormalities and their specific patterns across regions of interest (ROI) at the CT pixel level, it is important to normalize variation, utilize spatial information, and choose an efficient classification method. Decomposing CT images using a modified version of Aujol's algorithm, which was based on partial differential equations in negative Solobev space, facilitated normalization. Once decomposed, noise was removed to reduce variation, yielding a denoised image. Next, we used standard image texture features to capture spatial information within ROIs and to create the variables used to classify abnormalities. Abnormalities were classified via two methods, namely, multinomial logistic regressions (using backward selection and predicted probabilities) and non-concave penalized likelihood equations combined with support vector machine (SVM) classification. Initially we applied our techniques to classify patterns of Scleroderma-related lung abnormalities (ground glass, fibrosis and honeycombing) in small ROIs. Finally we extended the classification scheme to the whole lung, accounting for structure, in order to produce quantitative scores of sclerodermal abnormalities in lung parenchyma of patients.

ISBN: 9780549130444Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Classification in thoracic computed tomography image data.
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100 1 $a Kim, Hyun Jung. $3 1288442
245 1 0 $a Classification in thoracic computed tomography image data.
300 $a 146 p.
500 $a Adviser: Gang Li.
500 $a Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: .
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2007.
520 $a A challenge for computed tomography (CT) image analysis is to produce quantitative information of parenchymal abnormalities generalizable to a population of interest. Typically CT data from patient populations are heterogeneous, spatially correlated, massive and high dimensional. In order to classify abnormalities and their specific patterns across regions of interest (ROI) at the CT pixel level, it is important to normalize variation, utilize spatial information, and choose an efficient classification method. Decomposing CT images using a modified version of Aujol's algorithm, which was based on partial differential equations in negative Solobev space, facilitated normalization. Once decomposed, noise was removed to reduce variation, yielding a denoised image. Next, we used standard image texture features to capture spatial information within ROIs and to create the variables used to classify abnormalities. Abnormalities were classified via two methods, namely, multinomial logistic regressions (using backward selection and predicted probabilities) and non-concave penalized likelihood equations combined with support vector machine (SVM) classification. Initially we applied our techniques to classify patterns of Scleroderma-related lung abnormalities (ground glass, fibrosis and honeycombing) in small ROIs. Finally we extended the classification scheme to the whole lung, accounting for structure, in order to produce quantitative scores of sclerodermal abnormalities in lung parenchyma of patients.
590 $a School code: 0031.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biophysics, General. $3 1019105
650 4 $a Health Sciences, Radiology. $3 1019076
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710 2 0 $a University of California, Los Angeles. $3 626622
773 0 $t Dissertation Abstracts International $g 68-07B.
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791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3272275