No abstract

Tuberculosis is a health threatening disease with high mortality and morbidity rates. So proper tools are required to diagnose the disease at the right time. To address this issue, we propose a novel scheme for detecting tuberculosis in chest X-ray images. The method detects tuberculosis in a three stage process namely segmentation, feature extraction and classification. The lung region is segmented using adaptive thresholding. Then feature extraction extracts information contained in the image. These feature set is given to support vector machine to distinguish between normal and abnormal chest image. The algorithm is evaluated using four performance measuring criteria : accuracy, sensitivity, specificity and area under the ROC curve (AUC). Simulation results reveal the efficiency of the method in detecting tuberculosis.