Hierarchical Markov random field modeling for texture classification in chest radiographs

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Vargas-Voracek, R; Floyd, CE; Nolte, LW; McAdams, P

Published Date

  • December 1, 1996

Published In

Volume / Issue

  • 2710 /

Start / End Page

  • 679 - 685

International Standard Serial Number (ISSN)

  • 0277-786X

Digital Object Identifier (DOI)

  • 10.1117/12.237971

Citation Source

  • Scopus