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. © 2014 Springer International Publishing.
CITATION STYLE
Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2014). Automatic polyp detection using global geometric constraints and local intensity variation patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8674 LNCS, pp. 179–187). Springer Verlag. https://doi.org/10.1007/978-3-319-10470-6_23
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