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Proceedings Paper

Computer-based objective quantitative assessment of pulmonary parenchyma via x-ray CT
Author(s): Renuka Uppaluri; Geoffrey McLennan M.D.; Milan Sonka; Eric A. Hoffman
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Paper Abstract

This paper is a review of our recent studies using a texture- based tissue characterization method called the Adaptive Multiple Feature Method. This computerized method is automated and performs tissue classification based upon the training acquired on a set of representative examples. The AMFM has been applied to several different discrimination tasks including normal subjects, subjects with interstitial lung disease, smokers, asbestos-exposed subjects, and subjects with cystic fibrosis. The AMFM has also been applied to data acquired using different scanners and scanning protocols. The AMFM has shown to be successful and better than other existing techniques in discriminating the tissues under consideration. We demonstrate that the AMFM is considerably more sensitive and specific in characterizing the lung, especially in the presence of mixed pathology, as compared to more commonly used methods. Evidence is presented suggesting that the AMFM is highly sensitive to some of the earliest disease processes.

Paper Details

Date Published: 3 July 1998
PDF: 7 pages
Proc. SPIE 3337, Medical Imaging 1998: Physiology and Function from Multidimensional Images, (3 July 1998); doi: 10.1117/12.312585
Show Author Affiliations
Renuka Uppaluri, Univ. of Iowa College of Medicine (United States)
Geoffrey McLennan M.D., Univ. of Iowa College of Medicine (United States)
Milan Sonka, Univ. of Iowa (United States)
Eric A. Hoffman, Univ. of Iowa College of Medicine and Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 3337:
Medical Imaging 1998: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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