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 proceedingConference contribution

    11 Citations (Scopus)

    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 languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages179-187
    Number of pages9
    Volume8674 LNCS
    EditionPART 2
    ISBN (Print)9783319104690
    DOIs
    StatePublished - 2014
    Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
    Duration: Sep 14 2014Sep 18 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume8674 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
    CountryUnited States
    CityBoston, MA
    Period9/14/149/18/14

    Fingerprint

    Global Constraints
    Geometric Constraints
    Detectors
    Voting
    Descriptors
    Patch
    Detector
    Minimise
    Evaluation
    Demonstrate

    Keywords

    • boundary classification
    • edge voting
    • Optical colonoscopy
    • polyp detection

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    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) (PART 2 ed., Vol. 8674 LNCS, 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.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8674 LNCS 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 proceedingConference contribution

    Tajbakhsh, N, Gurudu, SR & 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). PART 2 edn, vol. 8674 LNCS, 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 8674 LNCS. 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). https://doi.org/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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8674 LNCS 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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    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.",
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