A classification-enhanced vote accumulation scheme for detecting colonic polyps

Nima Tajbakhsh, Suryakanth R. Gurudu, Jianming Liang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    17 Citations (Scopus)

    Abstract

    Colorectal cancer most often begins as abnormal growth of the colon wall, commonly referred to as polyps. It has been shown that the timely removal of polyps with optical colonoscopy (OC) significantly reduces the incidence and mortality of colorectal cancer. However, a significant number of polyps are missed during OC in clinical practice - the pooled miss-rate for all polyps is 22% (95% CI, 19%-26%). Computer-aided detection may offer promises of reducing polyp miss-rate. This paper proposes a new automatic polyp detection method. Given a colonoscopy image, the main idea is to identify the edge pixels that lie on the boundary of polyps and then determine the location of a polyp from the identified edges. To do so, we first use the Canny edge detector to form a crude set of edge pixels, and then apply a set of boundary classifiers to remove a large portion of irrelevant edges. The polyp locations are then determined by a novel vote accumulation scheme that operates on the positively classified edge pixels. We evaluate our method on 300 images from a publicly available database and obtain results superior to the state-of-the-art performance.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages53-62
    Number of pages10
    Volume8198 LNCS
    DOIs
    StatePublished - 2013
    Event5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - Nagoya, Japan
    Duration: Sep 22 2013Sep 22 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8198 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013
    CountryJapan
    CityNagoya
    Period9/22/139/22/13

    Fingerprint

    Vote
    Colorectal Cancer
    Pixel
    Pixels
    Computer-aided Detection
    Mortality
    Incidence
    Classifiers
    Classifier
    Detector
    Detectors
    Evaluate

    Keywords

    • boundary classification
    • Optical colonoscopy
    • polyp detection
    • random forest
    • voting scheme

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2013). A classification-enhanced vote accumulation scheme for detecting colonic polyps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8198 LNCS, pp. 53-62). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8198 LNCS). https://doi.org/10.1007/978-3-642-41083-3_7

    A classification-enhanced vote accumulation scheme for detecting colonic polyps. / 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. 8198 LNCS 2013. p. 53-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8198 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Tajbakhsh, N, Gurudu, SR & Liang, J 2013, A classification-enhanced vote accumulation scheme for detecting colonic polyps. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8198 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8198 LNCS, pp. 53-62, 5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-642-41083-3_7
    Tajbakhsh N, Gurudu SR, Liang J. A classification-enhanced vote accumulation scheme for detecting colonic polyps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8198 LNCS. 2013. p. 53-62. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-41083-3_7
    Tajbakhsh, Nima ; Gurudu, Suryakanth R. ; Liang, Jianming. / A classification-enhanced vote accumulation scheme for detecting colonic polyps. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8198 LNCS 2013. pp. 53-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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    N2 - Colorectal cancer most often begins as abnormal growth of the colon wall, commonly referred to as polyps. It has been shown that the timely removal of polyps with optical colonoscopy (OC) significantly reduces the incidence and mortality of colorectal cancer. However, a significant number of polyps are missed during OC in clinical practice - the pooled miss-rate for all polyps is 22% (95% CI, 19%-26%). Computer-aided detection may offer promises of reducing polyp miss-rate. This paper proposes a new automatic polyp detection method. Given a colonoscopy image, the main idea is to identify the edge pixels that lie on the boundary of polyps and then determine the location of a polyp from the identified edges. To do so, we first use the Canny edge detector to form a crude set of edge pixels, and then apply a set of boundary classifiers to remove a large portion of irrelevant edges. The polyp locations are then determined by a novel vote accumulation scheme that operates on the positively classified edge pixels. We evaluate our method on 300 images from a publicly available database and obtain results superior to the state-of-the-art performance.

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