Share Email Print

Proceedings Paper

Automatic polyp detection in colonoscopy videos
Author(s): Zijie Yuan; Mohammadhassan IzadyYazdanabadi; Divya Mokkapati; Rujuta Panvalkar; Jae Y. Shin; Nima Tajbakhsh; Suryakanth Gurudu; Jianming Liang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 24 February 2017
PDF: 10 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332K (24 February 2017); doi: 10.1117/12.2254671
Show Author Affiliations
Zijie Yuan, Arizona State Univ. (United States)
Mohammadhassan IzadyYazdanabadi, Arizona State Univ. (United States)
Divya Mokkapati, Arizona State Univ. (United States)
Rujuta Panvalkar, Arizona State Univ. (United States)
Jae Y. Shin, Arizona State Univ. (United States)
Nima Tajbakhsh, Arizona State Univ. (United States)
Suryakanth Gurudu, Mayo Clinic (United States)
Jianming Liang, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?