Matching colonic polyps using correlation optimized warping

Abstract

Computed tomographic colonography (CTC) combined with a computer aided detection system has the potential for improving colonic polyp detection and increasing the use of CTC for colon cancer screening. In the clinical use of CTC, a true colonic polyp will be confirmed with high confidence if a radiologist can find it on both the supine and prone scans. To assist radiologists in CTC reading, we propose a new method for matching polyp findings on the supine and prone scans. The method performs a colon registration using four automatically identified anatomical salient points and correlation optimized warping (COW) of colon centerline features. We first exclude false positive detections using prediction information from a support vector machine (SVM) classifier committee to reduce initial false positive pairs. Then each remaining CAD detection is mapped to the other scan using COW technique applied to the distance along the centerline in each colon. In the last step, a new SVM classifier is applied to the candidate pair dataset to find true polyp pairs between supine and prone scans. Experimental results show that our method can improve the sensitivity to 0.87 at 4 false positive pairs per patient compared with 0.72 for a competing method that uses the normalized distance along the colon centerline (p<0.01).