Computer-assisted diagnosis of chest radiographs for pneumoconioses

A Computer-assisted Chest Radiograph Reader System (CARRS) was developed for the detection of pathological features in lungs presenting with pneumoconioses. CARRS applies novel techniques in automatic image segmentation, incorporates neural network-based pattern classification, and integrates these into a graphical user interface. The three aspects of CARRS are described: Chest radiograph digitization and display, rib and parenchyma characterization, and classification. The quantization of the chest radiograph film was optimized to maximize the information content of the digital images. Entropy was used as the benchmark for optimizing the quantization. From the rib-segmented images, regions of interest were selected by the pulmonologist. A feature vector composed of image characteristics such as entropy, textural statistics, etc. was calculated. A laterally primed adaptive resonance theory (LA-PART) neural network was used as the classifier. LAPART classification accuracy averaged 86.8%. "Truth" was determined by the two pulmonologists. The CARRS has demonstrated potential as a screening device. Today, 90% or more of the chest radiographs seen by the pulmonologist are normal. A computer-based system that can screen 50% or more of the chest radiographs represents a large savings in time and dollars.