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Proceedings Paper

Hierarchical Markov random-field modeling for texture classification in chest radiographs
Author(s): Rene Vargas-Voracek; Carey E. Floyd; Loren W. Nolte; Page McAdams
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Paper Abstract

A hierarchical Markov random field (MRF) modeling approach is presented for the classification of textures in selected regions of interest (ROIs) of chest radiographs. The procedure integrates possible texture classes and their spatial definition with other components present in an image such as noise and background trend. Classification is performed as a maximum a-posteriori (MAP) estimation of texture class and involves an iterative Gibbs- sampling technique. Two cases are studied: classification of lung parenchyma versus bone and classification of normal lung parenchyma versus miliary tuberculosis (MTB). Accurate classification was obtained for all examined cases showing the potential of the proposed modeling approach for texture analysis of radiographic images.

Paper Details

Date Published: 16 April 1996
PDF: 7 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237971
Show Author Affiliations
Rene Vargas-Voracek, Duke Univ. Medical Ctr. (United States)
Carey E. Floyd, Duke Univ. Medical Ctr. (United States)
Loren W. Nolte, Duke Univ. (United States)
Page McAdams, Duke Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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