Enhanced automatic colon segmentation for better cancer diagnosis

Abstract

Colon segmentation is the first stage towards polyp detection, the main cause of colon cancer. Due to the immense importance of colon cancer diagnosis which is the second leading cause of death in the world, the segmentation phase must guarantee that no polyps are missed, especially the flat ones that are usually hard to detect. This work validates the 3D automated colon segmentation approach using the convex contour model previously proposed in literature. It also adds improvements to its pre-processing stage in order to better capture the colon walls and to enhance the results of the subsequent phases of the segmentation process. Experiments were conducted on 27 colon data sets that include 30 polyps. Moreover, 30 synthesized polyps with various shapes and sizes were placed at challenging areas of the colon's complex structure. Experiments conducted show a significant improvement in the construction of colon walls and the rate of polyp detection over that provided by the original technique.