Improvement of mass detection in mammogram using multi-view information

Xiaoming Liu; Ting Zhu; Leilei Zhai; Jun Liu

doi:10.1117/12.2244627

29 August 2016 Improvement of mass detection in mammogram using multi-view information

Xiaoming Liu, Ting Zhu, Leilei Zhai, Jun Liu

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100334M (2016) https://doi.org/10.1117/12.2244627
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

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.

Citation Download Citation

Xiaoming Liu, Ting Zhu, Leilei Zhai, and Jun Liu "Improvement of mass detection in mammogram using multi-view information", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334M (29 August 2016); https://doi.org/10.1117/12.2244627

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