Hierarchical Markov random-field modeling for texture classification in chest radiographs

Rene Vargas-Voracek; Carey E. Floyd Jr.; Loren W. Nolte; Page McAdams

doi:10.1117/12.237971

16 April 1996 Hierarchical Markov random-field modeling for texture classification in chest radiographs

Rene Vargas-Voracek, Carey E. Floyd Jr., Loren W. Nolte, Page McAdams

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237971
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

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.

Citation Download Citation

Rene Vargas-Voracek, Carey E. Floyd Jr., Loren W. Nolte, and Page McAdams "Hierarchical Markov random-field modeling for texture classification in chest radiographs", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237971

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