title: Part-based multiderivative edge cross-sectional profiles for polyp detection in colonoscopy.
abstract: This paper presents a novel technique for automated detection of protruding polyps in colonoscopy images using edge cross-section profiles (ECSP). We propose a part-based multiderivative ECSP that computes derivative functions of an edge cross-section profile and segments each of these profiles into parts. Therefore, we can model or extract features suitable for each part. Our features obtained from the parts can effectively describe complex properties of protruding polyps including the shape of the parts, texture, and protrusion and smoothness of the polyp surface. We evaluated our method against two existing polyp image detection techniques on 42 different polyps, including those with little protrusion. Each polyp has a large variation of appearance in viewing angles, light conditions, and scales in different images. The evaluation showed that our technique outperformed the existing techniques in both accuracy and analysis time. Our method has a higher area under the free-response receiver operating characteristic curve. For instance, when both techniques have a true positive rate for polyp image detection of 81.4%, the average number of false regions per image of our technique is 0.32 compared to 1.8 of the best existing technique under study. Additionally, our technique can precisely mark edges of candidate polyp regions as visual feedback. These results altogether indicate that our technique is promising to provide visual feedback of polyp regions in clinical practice.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.forename: Wallapak
authors.initials: W
authors.lastname: Tavanapong
authors.forename: Johnny
authors.initials: J
authors.lastname: Wong
authors.forename: JungHwan
authors.initials: J
authors.lastname: Oh
authors.forename: Piet C
authors.initials: PC
authors.lastname: de Groen
mesh_terms: D000465:Algorithms; D003111:Colonic Polyps / Q000175:diagnosis*; D003113:Colonoscopy / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*