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29 March 2007 Colonic wall thickness using level sets for CT virtual colonoscopy visual assessment and polyp detection
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The detection of polyps in virtual colonoscopy is an active area of research. One of the critical elements in detecting cancerous polyps using virtual colonoscopy, especially in conjunction with computer-aided detection, is the accurate segmentation of the colon wall. The large CT attenuation difference between the lumen and inner, mucosal layer of the colon wall makes the segmentation of the lumen easily performed by traditional threshold segmentation techniques. However, determining the location of the colon outer wall is often difficult due to the low contrast difference between the colon wall's outer serosal layer and the fat surrounding the colon. We have developed an automatic, level set based method to determine from a CT colonography scan the location of the colon inner boundary and the colon outer wall boundary. From the location of the inner and outer colon wall boundaries, the wall thickness throughout the colon can be computed. Color mapping of the wall thickness on the colon surface allows for easy visual determination of potential regions of interest. Since the colon wall tends to be thicker at polyp locations, potential polyps also can be detected automatically at sites of increased colon wall thickness. This method was validated on several CT colonography scans containing optical colonoscopy-proven polyps. The method accurately determined thicker colonic wall regions in areas where polyps are present in the ground truth datasets and detected the polyps at a false positive rate between 44.4% and 82.8% lower than a state-of-the-art curvature-based method for initial polyp detection.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert L. Van Uitert and Ronald M. Summers M.D. "Colonic wall thickness using level sets for CT virtual colonoscopy visual assessment and polyp detection", Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65110S (29 March 2007);

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