In this thesis, we present an automatic polyp detection approach that integrates knowledge from flow visualization techniques. Our primary goal was to compute additional characteristic polyp features that improve the CAD performance of existing polyp detection approaches by eliminating found false-positive polyp candidates. We found that surface principal curvature directions presented discriminating patterns on surface areas that belong to colonic polyps. These could be visualized using lines of curvatures. We developed novel approaches for generating lines of curvature on the colon wall. In order to provide sufficient shape information, lines of curvature were distributed using curvature-adaptive streamline seeding and spacing strategies. Geometric features of lines of curvature were used to differentiate between true polyp detections and false positives. The visualization of the colon wall was also enhanced by visualizing lines of curvature.