TY - JOUR
T1 - Hybrid Subspace Fusion for Microcalcification Clusters Detection
AU - Xinsheng Zhang, Hongyan He, Naining Cao & Zhengshan Luo
JO - Journal of Fiber Bioengineering and Informatics
VL - 1
SP - 161
EP - 169
PY - 2015
DA - 2015/08
SN - 8
DO - http://doi.org/10.3993/jfbi03201516
UR - https://global-sci.org/intro/article_detail/jfbi/4696.html
KW - Subspace Learning
KW - Data Fusion
KW - Micalcification Cluster
KW - Support Vector Machine
KW - Digital Mammograms
AB - 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.