IDENTIFICATION OF LUNG REGIONS IN CHEST RADIOGRAPHS USING MARKOV RANDOM-FIELD MODELING

The authors present an algorithm utilizing Markov random field modelin g for identifying lung regions in a digitized chest radiograph (DCR). Let x represent the classifications of each pixel in a DCR as either l ung or nonlung. We model x as a realization of a spatially varying Mar kov random field. This model is developed utilizing spatial and textur al 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 yie lding 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 int o the meaning and level of the algorithm's performance numbers, the re sults are compared to those of some easily implemented classification algorithms. (C) 1998 American Association of Physicists in Medicine. [ S0094-2405(98)02206-8].