title: Automatic polyp detection using global geometric constraints and local intensity variation patterns.
abstract: This paper presents a new method for detecting polyps in colonoscopy. Its novelty lies in integrating the global geometric constraints of polyps with the local patterns of intensity variation across polyp boundaries: the former drives the detector towards the objects with curvy boundaries, while the latter minimizes the misleading effects of polyp-like structures. This paper makes three original contributions: (1) a fast and discriminative patch descriptor for precisely characterizing patterns of intensity variation across boundaries, (2) a new 2-stage classification scheme for accurately excluding non-polyp edges from an overcomplete edge map, and (3) a novel voting scheme for robustly localizing polyps from the retained edges. Evaluations on a public database and our own videos demonstrate that our method is promising and outperforms the state-of-the-art methods.
authors.forename: Nima
authors.initials: N
authors.lastname: Tajbakhsh
authors.forename: Suryakanth R
authors.initials: SR
authors.lastname: Gurudu
authors.forename: Jianming
authors.initials: J
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000473:pathology*; D003113:Colonoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity