Computer-aided detection of colitis on computed tomography using a visual codebook

Colitis is inflammation of the colon that is frequently associated with infection and immune compromise. In this paper, we propose an automatic method for colitis detection in abdominal CT scans. We first used a visual codebook constructed by clustering feature vectors from a set of training image patches to detect the suspicious colitis regions. The initial detections included false detection points located in various organs including muscle, kidney and liver. We reduced the false positives by applying masks of these regions obtained from whole-organ segmentation. We tested our method on a CT dataset with 20 cases of colitis and 15 non-colitis cases. Average detected lesion volume for positive cases is 205ml; for negative cases is 97ml. Sixteen out of the 22 positive cases were correctly identified, yielding a sensitivity of 72.7%; 4 out of 15 negative cases were incorrectly identified, yielding a specificity of 73.3%.