Year: 2015
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 1 : pp. 161–169
Abstract
Early detection of breast cancer, a significant public health problem in the world, is the key for improving breast cancer early prognosis. Mammography is considered the most reliable and widely used diagnostic technique for early detection of breast cancer. However, it is difficult for radiologists to perform both accurate and uniform evaluation for the enormous mammograms with widespread screening. Microcalcification clusters is one of the most important clue of the breast cancer, and their automated detection is very helpful for early breast cancer diagnosis. Because of the poor quality of the mammographic images and the small size of the microcalcifications, it is a very difficult task to perform detecting the early breast cancer. In this paper, we propose a novel approach based on hybrid subspace fusion for detection microcalcification clusters, and successfully apply it to detection task in digital mammograms. In such a system, subspace learning algorithms will be selectively fused according to the ability of preserving the classification information. Experimental results show that the proposed method improved the performance and stability of microcalcification cluster detection and could be adapt to the noise environments better. The proposed methods could get satisfactory results on sensitivity and reduce false positive rate, which provide some new ideas and methods for the research and development of computer-aided detection system in the breast cancer detection community.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbi03201516
Journal of Fiber Bioengineering and Informatics, Vol. 8 (2015), Iss. 1 : pp. 161–169
Published online: 2015-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 9
Keywords: Subspace Learning
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Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection
Wang, Huiya
Feng, Jun
Wang, Hongyu
Ciaccio, Edward J.
Liu, Feng
Technology and Health Care, Vol. 25 (2017), Iss. P.325
https://doi.org/10.3233/THC-171336 [Citations: 0]