Automatic polyp detection using global geometric constraints and local intensity variation patterns

Nima Tajbakhsh; Suryakanth R. Gurudu; Jianming Liang

doi:10.1007/978-3-319-10470-6_23

Automatic polyp detection using global geometric constraints and local intensity variation patterns

Nima Tajbakhsh, Suryakanth R. Gurudu, Jianming Liang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Scopus citations

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.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
Publisher	Springer Verlag
Pages	179-187
Number of pages	9
Edition	PART 2
ISBN (Print)	9783319104690
DOIs	https://doi.org/10.1007/978-3-319-10470-6_23
State	Published - Jan 1 2014
Event	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States Duration: Sep 14 2014 → Sep 18 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8674 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Country/Territory	United States
City	Boston, MA
Period	9/14/14 → 9/18/14

Keywords

Optical colonoscopy
boundary classification
edge voting
polyp detection

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-10470-6_23

Cite this

Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2014). Automatic polyp detection using global geometric constraints and local intensity variation patterns. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings (PART 2 ed., pp. 179-187). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8674 LNCS, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-319-10470-6_23

Automatic polyp detection using global geometric constraints and local intensity variation patterns. / Tajbakhsh, Nima; Gurudu, Suryakanth R.; Liang, Jianming.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 2. ed. Springer Verlag, 2014. p. 179-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8674 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tajbakhsh, N, Gurudu, SR & Liang, J 2014, Automatic polyp detection using global geometric constraints and local intensity variation patterns. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8674 LNCS, Springer Verlag, pp. 179-187, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, Boston, MA, United States, 9/14/14. https://doi.org/10.1007/978-3-319-10470-6_23

Tajbakhsh N, Gurudu SR, Liang J. Automatic polyp detection using global geometric constraints and local intensity variation patterns. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 2 ed. Springer Verlag. 2014. p. 179-187. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-319-10470-6_23

Tajbakhsh, Nima ; Gurudu, Suryakanth R. ; Liang, Jianming. / Automatic polyp detection using global geometric constraints and local intensity variation patterns. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 2. ed. Springer Verlag, 2014. pp. 179-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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AB - 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.

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Automatic polyp detection using global geometric constraints and local intensity variation patterns

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