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Markov random field modeling in posteroanterior chest radiograph segmentation
Author(s) -
Vittitoe Neal F.,
VargasVoracek Rene,
Floyd Carey E.
Publication year - 1999
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598673
Subject(s) - pixel , markov random field , segmentation , conditional random field , chest radiograph , artificial intelligence , pattern recognition (psychology) , image segmentation , markov chain , computer science , algorithm , random field , mathematics , radiography , statistics , medicine , radiology
Previously, the authors presented an algorithm that identifies lung regions in a digitized posteroanterior chest radiograph (DCR) by labeling each pixel as either lung or nonlung. In this manuscript, the inherent flexibility of this algorithm is demonstrated as the algorithm is generalized to identify multiple anatomical regions in a DCR. Specifically, each pixel is classified as belonging to one of six anatomical region types: lung, subdiaphragm, heart, mediastinum, body, or background. The algorithm determines the optimal set of pixel classifications, x OPT , for a given set of DCR pixel gray level values y via a probabilistic approach that defines x OPTas the particular segmentation that maximizes the conditional distribution P ( x | y ) . A spatially varying Markov random field (MRF) model is used that incorporates spatial and textural information of each possible region type. MRF modeling provides the form of P ( x | y ) , and Iterated Conditional Modes is used to converge to the distribution maximum of P ( x | y ) thus obtaining the optimal segmentation for a given DCR. Results show the algorithm being able to correctly classify 90.0%±3.4% of the pixels in a DCR.