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- W2022377467 abstract "ABSTRACT A hierarchical Markov random field (MRF) modeling approach is presented for the classification of textures inselected 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. Classificationis 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. Keywords: Texture analysis, Markov random fields, Gibbs sampling, medical image processing, radiographicimage processing.O81942O859/96/$6.O0 SPIE Vol. 2710 / 679Downloaded From: http://proceedings.spiedigitallibrary.org/ on 02/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx" @default.
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- W2022377467 title "<title>Hierarchical Markov random-field modeling for texture classification in chest radiographs</title>" @default.
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