Automatic polyp detection in colonoscopy videos

Zijie Yuan, Mohammadhassan Izadyyazdanabadi, Divya Mokkapati, Rujuta Panvalkar, Jae Y. Shin, Nima Tajbakhsh, Suryakanth Gurudu, Jianming Liang

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

    25 Scopus citations


    Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

    Original languageEnglish (US)
    Title of host publicationMedical Imaging 2017
    Subtitle of host publicationImage Processing
    EditorsElsa D. Angelini, Martin A. Styner, Elsa D. Angelini
    ISBN (Electronic)9781510607118
    StatePublished - 2017
    EventMedical Imaging 2017: Image Processing - Orlando, United States
    Duration: Feb 12 2017Feb 14 2017

    Publication series

    NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
    ISSN (Print)1605-7422


    OtherMedical Imaging 2017: Image Processing
    Country/TerritoryUnited States


    • Colonoscopy
    • Computer-aided detection
    • Convolutional neural networks (CNNs)
    • Deep learning
    • Polyp

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Biomaterials
    • Atomic and Molecular Physics, and Optics
    • Radiology Nuclear Medicine and imaging


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