Hybrid Subspace Fusion for Microcalcification Clusters Detection (Journal of Fiber Bioengineering and Informatics, 2015)
Early detection of breast cancer, a significant public health problem in the world, is the key for\rimproving breast cancer early prognosis. Mammography is considered the most reliable and widely\rused diagnostic technique for early detection of breast cancer. However, it is difficult for radiologists\rto perform both accurate and uniform evaluation for the enormous mammograms with widespread\rscreening. Microcalcification clusters is one of the most important clue of the breast cancer, and their\rautomated detection is very helpful for early breast cancer diagnosis. Because of the poor quality of the\rmammographic images and the small size of the microcalcifications, it is a very difficult task to perform\rdetecting the early breast cancer. In this paper, we propose a novel approach based on hybrid subspace\rfusion for detection microcalcification clusters, and successfully apply it to detection task in digital\rmammograms. In such a system, subspace learning algorithms will be selectively fused according to the\rability of preserving the classification information. Experimental results show that the proposed method\rimproved the performance and stability of microcalcification cluster detection and could be adapt to\rthe noise environments better. The proposed methods could get satisfactory results on sensitivity and\rreduce false positive rate, which provide some new ideas and methods for the research and development\rof computer-aided detection system in the breast cancer detection community.