Polyp Segmentation Method for CT Colonography Computer Aided Detection

We have developed a new method employing the Canny edge detector and Radon transformation to segment images of polyp candidates for CT colonography (CTC) computer aided polyp detection and obtain features useful for distinguishing true polyps from false positive detections. The technique is applied to two-dimensional subimages of polyp candidates selected using various 3-D shape and curvature characteristics. We detect boundaries using the Canny operator. The baseline of the colon wall is detected by applying the Radon transform to the edge image and locating the strongest peak in the resulting transform matrix. The following features are calculated and used to classify detections as true positives (TP) and false positives (FP): polyp boundary length, polyp base length, polyp internal area, average intensity, polyp height, and inscribed circle radius. The segmentation technique was applied to a data set of 15 polyps larger than 3 mm and 617 false positives taken from 80 CTC studies (supine and prone screening of 40 patients). The sensitivity was 100% (15 of 15). 58% of the FP's were eliminated leaving an average of 3 false positives per study. Our method is able to segment polyps and quantitatively measure polyp features independently of orientation and shape.