Identification of lung regions in chest radiographs using Markov random field modeling.
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.
Vittitoe
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NF
Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA. nfv@deckard.mc.duke.edu
Vargas-Voracek
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R
Floyd
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CF
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D003936:Diagnosis, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008390:Markov Chains; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011856:Radiographic Image Enhancement / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods* / Q000706:statistics & numerical data; D013679:Technology, Radiologic