Segmentation of colon CT images is the main factor that inhibits automation of virtual colonoscopy. There are two main reasons that make efficient colon segmentation difficult. First, besides the colon, the small bowel, lungs, and stomach are also gas-filled organs in the abdomen. Second, peristalsis or residual feces often obstruct the colon, so that it consists of multiple gas-filled segments. In virtual colonoscopy, it is very useful to automatically connect the centerlines of these segments into a single colon centerline. Unfortunately, in some cases this is a difficult task. In this study a novel method for automated colon segmentation and connection of colon segments' centerlines is proposed. The method successfully combines features of segments, such as centerline and thickness, with information on main colon segments. The results on twenty colon cases show that the method performs well in cases of small obstructions of the colon. Larger obstructions are mostly also resolved properly, especially if they do not appear in the sigmoid part of the colon. Obstructions in the sigmoid part of the colon sometimes cause improper classification of the small bowel segments. If a segment is too small, it is classified as the small bowel segment. However, such misclassifications have little impact on colon analysis.