Matching colonic polyps from prone and supine CT colonography scans based on statistical curvature information

Abstract

Computed tomographic colonography (CTC) provides a feasible way for the detection of colorectal polyps and cancer screening. In the clinical practice of CTC, a true colonic polyp will be confirmed with high confidence if a radiologist can find it in both the supine and prone scans. To assist radiologists in CTC reading, we propose a new colonic polyp matching method based on statistical curvature information of polyp candidates. We first extract histograms of curvature-related features (HCF) from each polyp candidate, then use diffusion map to embed the original high dimensional data into a low-dimensional space. Experimental results show that by using our HCF method, we can improve the sensitivity from 0.58 to 0.74 at false positive rate 0.1 compared with a traditional method that uses only means of curvature-related features.