Colon cancer is one of the most frequent causes of death. CT colonography is a novel method for the detection of polyps and early cancer. The general principle of CT colonography includes a cathartic bowel preparation. The resulting discomfort for patients leads to limited patient acceptance and therefore to limited cancer detection rates. Reduced bowel preparation, techniques for stool tagging, and electronic cleansing, however, improve the acceptance rates. Hereby, the high density of oral contrast material highlights residual stool and can be digitally removed. Known subtraction methods cause artifacts: additional 3D objects are introduced and small bowel folds are perforated. We propose a new algorithm that is based on the 2nd derivative of the image data using the Hessian matrix and the following principal axis transform to detect tiny folds which shall not be subtracted together with tagged stool found by a thresholding method. Since the stool is usually not homogenously tagged with contrast media a detection algorithm for island-like structures is incorporated. The interfaces of air-stool level and colon wall are detected by a 3-dimensional difference of Gaussian module. A 3-dimensional filter smoothes the transitions between removed stool and colon tissue. We evaluated the efficacy of the new algorithm with 10 patient data sets. The results showed no introduced artificial objects and no perforated folds. The artifacts at the air-stool and colon tissue-stool transitions are considerably reduced compared to those known from the literature.
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