Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

David S. Paik, Stanford University
Christopher F. Beaulieu, Stanford University
Geoffrey D. Rubin, Stanford University
Burak Acar, Boğaziçi Üniversitesi
R. Brooke Jeffrey, Stanford University
Judy Yee, University of California, San Francisco
Joyoni Dey, Stanford University
Sandy Napel, Stanford University


We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.