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Early diagnosis and removal of colonic polyps is effective in the elimination of subsequent carcinoma. This paper presents a new approach for computer-aided detection of polyps. The approach mimics the way the radiologists view CT abdomen images and utilizes several geometric attributes obtained from many triples of mutually orthogonal planes. The histogram of the attributes obtained from a sufficiently large number of perpendicular random images serves as a robust signature to represent the shape. We combine the new 3-D pattern recognition with a support vector machine classifier, and show that the number or the false positive detections in the initial polyp detection studies can be substantially reduced. One of the main contributions of this study is the thorough analysis of planar geometrical attributes. When an appropriate combination of planar attributes is used, the false positive rate is reduced by 87 percent beyond that of the initial stage detector, while maintaining a sensitivity level of 95 percent. Using such methods, radiologists should be able to view CTC data much more efficiently and accurately than without CAD.