Computed tomography texture analysis capabilities in diagnosing a chronic obstructive pulmonary disease

The possibility of application of different textural features for the lung disease automatic diagnosis on the basis of the 2D digital computed tomography (CT) images was studied. Histogram features, covariance features, Haralick’s features and run length features were used. A procedure based on the discriminant analysis criterion was used for the selection of the best features group. We experimentally showed that the approach offered is convenient to use for solving the problem of automatic diagnosis on a 160-image set received during examination of patients with a chronic obstructive pulmonary disease. The resulting group of effective features includes two Haralick’s features and three run length features, providing the error rate of 0.11, which is better than similar results obtained without a feature selection procedure.