Share Email Print
cover

Proceedings Paper

Improvement of mass detection in mammogram using multi-view information
Author(s): Xiaoming Liu; Ting Zhu; Leilei Zhai; Jun Liu
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

Computer-aided diagnosis (CAD) system is helpful for lesion detection. In this study, we proposed a new mass detection method with analysis of bilateral mammograms. First of all, the mass candidates were detected in single view. To utilize the information in dual view, we match corresponding regions in mediolateral oblique (MLO) and craniocaudally (CC) views of the breast. In this paper, we introduced twin support vector machines (TWSVM) as classifier for mass detection, and proposed a new method for feature selection called multiple twin support vector machines (MTWSVM-RFE) to improve the accuracy of detection.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334M (29 August 2016); doi: 10.1117/12.2244627
Show Author Affiliations
Xiaoming Liu, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)
Ting Zhu, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)
Leilei Zhai, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)
Jun Liu, Wuhan Univ. of Science and Technology (China)
Hubei Province Key Lab. of Intelligent Information Processing and Real-time Industrial System (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top