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Proceedings Paper

Texture analysis of pulmonary parenchyma in normal and emphysematous lung
Author(s): Renuka Uppaluri; Theophano Mitsa; Eric A. Hoffman; Geoffrey McLennan; Milan Sonka
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Paper Abstract

Tissue characterization using texture analysis is gaining increasing importance in medical imaging. We present a completely automated method for discriminating between normal and emphysematous regions from CT images. This method involves extracting seventeen features which are based on statistical, hybrid and fractal texture models. The best subset of features is derived from the training set using the divergence technique. A minimum distance classifier is used to classify the samples into one of the two classes--normal and emphysema. Sensitivity and specificity and accuracy values achieved were 80% or greater in most cases proving that texture analysis holds great promise in identifying emphysema.

Paper Details

Date Published: 8 April 1996
PDF: 12 pages
Proc. SPIE 2709, Medical Imaging 1996: Physiology and Function from Multidimensional Images, (8 April 1996); doi: 10.1117/12.237888
Show Author Affiliations
Renuka Uppaluri, Univ. of Iowa (United States)
Theophano Mitsa, Univ. of Iowa (United States)
Eric A. Hoffman, Univ. of Iowa College of Medicine (United States)
Geoffrey McLennan, Univ. of Iowa College of Medicine (United States)
Milan Sonka, Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 2709:
Medical Imaging 1996: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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