Automatic segmentation of the colon for virtual colonoscopy

C. L. Wyatt, Y. Ge, D. J. Vining

    Research output: Contribution to journalArticle

    61 Scopus citations


    Virtual colonoscopy is a minimally invasive technique that enables early detection of colorectal polyps and cancer. Normally, a patient's bowel is prepared with colonic lavage and gas insufflation prior to computed tomography scanning. An important step for 3D analysis of the image volume is segmentation of the colon. The high-contrast gas/tissue interface that exists in the colon lumen makes segmentation of the majority of the colon relatively easy; however, two factors inhibit automatic segmentation of the entire colon. First, the colon is not the only gas-filled organ in the data volume: lungs, small bowel, and stomach also meet this criterion. User-defined seed points placed in the colon lumen have previously been required to spatially isolate the colon. Second, portions of the colon lumen may be obstructed by peristalsis, large masses, and/or residual feces. These complicating factors require increased user interaction during the segmentation process to isolate additional colonic segments. To automate the segmentation of the colon, we have developed a method to locate seed points and segment the gas-filled lumen sections without user supervision. We have also developed an automated approach to improve lumen segmentation by digitally removing residual contrast-enhanced fluid. Experimental results with 20 patient volumes show that our method is accurate and reliable. (C) 2000 Elsevier Science Ltd.

    Original languageEnglish (US)
    Pages (from-to)1-9
    Number of pages9
    JournalComputerized Medical Imaging and Graphics
    Issue number1
    StatePublished - Feb 1 2000


    • Distance transforms
    • Image analysis
    • Region growing
    • Segmentation
    • Virtual colonoscopy

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design

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