A statistical approach for robust polyp detection in CT colonography.

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the statistical features derived from the local colonic surface that are used for the detection of colonic polyps in computed tomography (CT) colonography. The candidate surface voxels were detected and clustered using the surface normal intersection, convexity test, region growing and Hough Transform. The main objective of this paper is the selection of the statistical features that optimally capture the convexity of the candidate surface and consequently provide a high discrimination between local surfaces defined by polyps and folds. The developed polyp detection scheme is computationally efficient (typically takes 3.9 minute per dataset) and shows 100% sensitivity for phantom polyps greater than 5mm and 87.5% sensitivity for real polyps greater than 5mm with an average of 4.05 false positives per dataset.