Automatic polyp detection from learned boundaries

Nima Tajbakhsh, Changching Chi, Suryakanth R. Gurudu, Jianming Liang

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

    14 Scopus citations

    Abstract

    Colonoscopy is the primary method for detecting and removing polyps-precursors to colon cancer, but during colonoscopy, a significant number of polyps are missed-the pooled miss-rate for all polyps is 22% (95% CI, 19%-26%). This paper presents an automatic polyp detection system for colonoscopy, aiming to alert colonoscopists to possible polyps during the procedures. Given an input image, our method first collects a crude set of edge pixels, then refines this edge map by effectively removing many non-polyp boundary edges through a classification scheme, and finally localizes polyps based on the retained edges with a novel voting scheme. This paper makes three original contributions: (1) a fast and discriminative patch descriptor for precisely characterizing image appearance, (2) a new 2-stage classification pipeline for accurately excluding undesired edges, and (3) a novel voting scheme for robustly localizing polyps from fragmented edge maps. Evaluations demonstrate that our method outperforms the state-of-the-art.

    Original languageEnglish (US)
    Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages97-100
    Number of pages4
    ISBN (Electronic)9781467319591
    DOIs
    StatePublished - Jul 29 2014
    Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
    Duration: Apr 29 2014May 2 2014

    Publication series

    Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

    Other

    Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
    CountryChina
    CityBeijing
    Period4/29/145/2/14

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

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  • Cite this

    Tajbakhsh, N., Chi, C., Gurudu, S. R., & Liang, J. (2014). Automatic polyp detection from learned boundaries. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 97-100). [6867818] (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6867818