Share Email Print
cover

Proceedings Paper • new

Polyp classification by Weber’s Law as texture descriptor for clinical colonoscopy
Format Member Price Non-Member Price
PDF $17.00 $21.00
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

Weber’s law for image feature descriptor (WLD) is based on the theory that the ratio of the increment threshold to the background intensity is a constant. It has been used in facial recognition, structure detection, and tissue classification in X-ray images. In this paper, WLD is explored in the polyp classification in color colonoscopy images for the first time. An open, on-line colonoscopy image database is used to evaluate the new descriptor. The database contains 74 polyps, including 19 benign polyps and 55 malignant ones. Each polyp has a white light image (WLI) and a narrow band image (NBI), both were obtained by the same fibro-colonoscopy from the same patient. WLD image texture features are extracted from three color channels of (1) color WLI, (2) color NBI and (3) WLI+NBI. The extracted features are analyzed, ranked and classified using a Random Forest package based on the merit of the area under the curve (AUC) of the Receiver Operating Characteristics (ROC). The performance of WLD is quantitatively documented by the AUC, the ROC curve, the P-R (precision-recall) plot and the accuracy measure with comparison to commonly used features, such as Haralick and local binary pattern feature descriptors. The results demonstrate the advantage of WLD in the polyp classification in terms of the quantitative measures.

Paper Details

Date Published: 13 March 2019
PDF: 5 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502V (13 March 2019); doi: 10.1117/12.2512756
Show Author Affiliations
Yi Wang, The 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, The 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, The 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