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Identification of lung regions in chest radiographs using Markov random field modeling
Author(s) -
Vittitoe Neal F.,
VargasVoracek Rene,
Floyd Carey E.
Publication year - 1998
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 - 10.1118/1.598405
Subject(s) - identification (biology) , radiography , markov random field , markov chain , markov model , random field , medicine , markov process , radiology , computer science , medical physics , artificial intelligence , mathematics , statistics , machine learning , image segmentation , image (mathematics) , botany , biology
The authors present an algorithm utilizing Markov random field modeling for identifying lung regions in a digitized chest radiograph (DCR). Let x represent the classifications of each pixel in a DCR as either lung or nonlung. We model x as a realization of a spatially varying Markov random field. This model is developed utilizing spatial and textural information extracted from samples of lung and nonlung region‐types in a training set of DCRs. With this model, the technique of Iterated Conditional Modes is used to determine the optimal classification of each pixel in a DCR. The algorithm's ability to identify lung regions is evaluated on a testing set of DCRs. The algorithm performs well yielding a sensitivity of 90.7 % ± 4.4 % , a specificity of 97.2 % ± 2.0 % , and an accuracy of 94.8 % ± 1.6 % . In an attempt to gain insight into the meaning and level of the algorithm's performance numbers, the results are compared to those of some easily implemented classification algorithms.