Automated small bowel segmentation is essential for computer-aided diagnosis (CAD) of small bowel pathology, such as tumor detection and pre-operative planning. We previously proposed a method to segment the small bowel using the mesenteric vasculature as a roadmap. The method performed well on small bowel segmentation but produced many false positives, most of which were located on the colon. To improve the accuracy of small bowel segmentation, we propose a semi-automated method with minimum interaction to distinguish the colon from the small bowel. The method utilizes anatomic knowledge about the mesenteric vasculature and a statistical method of colon detection. First, anatomic labeling of the mesenteric arteries is used to identify the arteries supplying the colon. Second, a statistical detector is created by combining two colon probability maps. One probability map is of the colon location and is generated from colon centerlines generated from CT colonography (CTC) data. Another probability map is of 3D colon texture using Haralick features and support vector machine (SVM) classifiers. The two probability maps are combined to localize colon regions, i.e., voxels having high probabilities on both maps were labeled as colon. Third, colon regions identified by anatomical labeling and the statistical detector are removed from the original results of small bowel segmentation. The method was evaluated on 11 abdominal CT scans of patients suspected of having carcinoid tumors. The reference standard consisted of manually-labeled small bowel segmentation. The method reduced the voxel-based false positive rate of small bowel segmentation from 19.7%±3.9% to 5.9%±2.3%, with two-tailed P-value < 0.0001.