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Proceedings Paper

Adaptive multiple feature method (AMFM) for early detecton of parenchymal pathology in a smoking population
Author(s): Renuka Uppaluri; Geoffrey McLennan M.D.; Paul Enright; James Standen; Pamela Boyer-Pfersdorf; Eric A. Hoffman
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Paper Abstract

Application of the Adaptive Multiple Feature Method (AMFM) to identify early changes in a smoking population is discussed. This method was specifically applied to determine if differences in CT images of smokers (with normal lung function) and non-smokers (with normal lung function) could be found through computerized texture analysis. Results demonstrated that these groups could be differentiated with over 80.0% accuracy. Further, differences on CT images between normal appearing lung from non-smokers (with normal lung function) and normal appearing lung from smokers (with abnormal lung function) were also investigated. These groups were differentiated with over 89.5% accuracy. In analyzing the whole lung region by region, the AMFM characterized 38.6% of a smoker lung (with normal lung function) as mild emphysema. We can conclude that the AMFM detects parenchymal patterns in the lungs of smokers which are different from normal patterns occurring in healthy non-smokers. These patterns could perhaps indicate early smoking-related changes.

Paper Details

Date Published: 3 July 1998
PDF: 6 pages
Proc. SPIE 3337, Medical Imaging 1998: Physiology and Function from Multidimensional Images, (3 July 1998); doi: 10.1117/12.312563
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)
Paul Enright, Univ. of Arizona College of Medicine (United States)
James Standen, Univ. of Arizona College of Medicine (United States)
Pamela Boyer-Pfersdorf, Univ. of Arizona College of Medicine (United States)
Eric A. Hoffman, Univ. of Iowa College of Medicine (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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