CT angiography (CTA) is increasingly used for vascular disease assessment because of its non-invasive characteristics. In order to get a comprehensive overview of the vascular anatomy, the bone has to be removed since it occludes the cranial vessels. One of the commonly used algorithms is bone subtraction, which obtains vessel images by subtracting pre-contrast images from post-contrast images. The current problems are that it removes too much vessel and sometimes pieces of bone still exist near vessel. The purpose of this study is to provide radiologist with a fuzzy technique tool to add back parts of missing vessel. A seed point is put on part of the vessel that was not well preserved and an area is selected to restrict the vessel growing. Vessel extraction is based on fuzzy-connectedness technique proposed by Udupa in 1996. Incorporating intensity information from both pre-contrast and post-contrast images creates the membership images. The value of each voxel in the membership images represents strength of fuzzy connectedness. The bigger the strength value, the more likely the voxel belongs to the classified vessel. After choosing a threshold for the strength, the vessel is extracted and added back. This method may also apply to the whole images to segment out the bone and the vessel. The study will improve the current vessel extraction and bone removal algorithms and provide a good tool for aiding radiologist to diagnose vascular diseases.