Fully automated scoring of chest radiographs in cystic fibrosis.

We present a prototype of a fully automated scoring system for chest radiographs (CXRs) in cystic fibrosis. The system was used to analyze real, clinical CXR data, to estimate the Shwachman-Kulczycki score for the image. Images were resampled and normalized to a standard size and intensity level, then segmented with a patch-based nearest-neighbor mapping algorithm. Texture features were calculated regionally and globally, using Tamura features, local binary patterns (LBP), gray-level co-occurrence matrix and Gabor filtering. Feature selection was guided by current understanding of the disease process, in particular the reorganization and thickening of airways. Combinations of these features were used as inputs for support vector machine (SVM) learning to classify each CXR, and evaluated using two-fold cross-validation for agreement with clinician scoring. The final computed score for each image was compared with the score assigned by a physician. Using this prototype system, we analyzed 139 CXRs from an Australian pediatric cystic fibrosis registry, for which texture directionality showed greatest discriminating power. Computed scores agreed with clinician scores in 75% of cases, and up to 90% of cases in discriminating severe disease from mild disease, similar to the level of human interobserver agreement for this dataset.