Share Email Print
cover

Proceedings Paper • new

Differentiation of polyps by clinical colonoscopy via integrated color information, image derivatives and machine learning
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Clinical colonoscopy is currently the gold standard for polyp detection and resection. Both white light images (WLI) and narrow band images (NBI) could be obtained by the fibro-colonoscope from the same patient and currently used as a diagnosing reference for differentiation of hyperplastic polyps from adenomas. In this paper, we investigate the performance of WLI and NBI in different color spaces for polyp classification. A Haralick model with 30 co-occurrence matrix features is used in our experiments on 74 polyps, including 19 hyperplastic polyps and 55 adenomatous ones. The features are extracted from different color channels in each of three color spaces (RGB, HSV, chromaticity) and different derivative (intensity, gradient and curvature) images. The features from each derivative image in each color space are classified. The classification results from all the color spaces and all the derive images are input to a greedy machine learning program to verify the necessity of the integration of derivative image data and different color spaces. The feature classification and machine learning are implemented by the use of the Random Forest package. The wellknown area under the receiver operating characteristics curve is calculated to quantify the performances. The experiments validated the advantage of using the integration of the three derivatives of WLI and NBI and the three different color spaces for polyp classification.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502Y (13 March 2019); doi: 10.1117/12.2512578
Show Author Affiliations
Yi Wang, State Univ. of New York, Stony Brook (United States)
Tianjin Univ. (China)
Marc Pomeroy, State Univ. of New York, Stony Brook (United States)
Weiguo Cao, State Univ. of New York, Stony Brook (United States)
Yongfeng Gao, State Univ. of New York, Stony Brook (United States)
Edward Sun, State Univ. of New York, Stony Brook (United States)
Samuel Stanley III, Washington Univ. in St. Louis (United States)
Juan Carlos Bucobo, State Univ. of New York, Stony Brook (United States)
Zhengrong Liang, State Univ. of New York, Stony Brook (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

© SPIE. Terms of Use
Back to Top