%0 Journal Article
%T A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms
%A Xiaoyong Zhang
%A Noriyasu Homma
%A Shotaro Goto
%A Yosuke Kawasumi
%A Tadashi Ishibashi
%A Makoto Abe
%A Norihiro Sugita
%A Makoto Yoshizawa
%J Journal of Medical Engineering
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/615254
%X The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a multilevel wavelet reconstruction approach. Finally, MCs are detected based on their distributions feature. Experiments are performed on 138 clinical mammograms. The proposed method is capable of detecting 92.9% of true microcalcification clusters with an average of 0.08 false microcalcification clusters detected per image. 1. Introduction Breast cancer is one of the major causes of mortality in middle-aged women, especially in developed countries [1]. At present, there are no effective ways to prevent breast cancer since its cause remains unknown [2]. Therefore, early detection becomes the key to improving the breast cancer prognosis and reducing the mortality rates. Mammography has been widely recognized as being one of the most effective imaging modalities for early detection of breast cancer. However, it is a hard work for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. A computer-aided detection or diagnosis (CAD) system, which uses computer technologies to detect the typical signs of breast cancer, has been developed to provide a Ħ°second opinionĦħ for radiologists and to improve the accuracy and stability of diagnosis. In general, there are three signs of breast cancer in a mammogram: microcalcification clusters (MCs), architectural distortions, and masses [2]. In this paper, we particularly focus on the detection of MCs since they appear in 30¨C50% of mammographic diagnosed cases and show a high correlation with breast cancer [3]. According to the Breast Image Reporting and Data System (BI-RADS) lexicon [4], MCs are tiny calcium deposits that appear as small bright spots in mammograms. As an example, Figure 1 shows an MC in a mediolateral-oblique (MLO) mammogram. It is often hard for radiologists to find individual MCs in mammograms because they are very small (typically, 0.05¨C1£żmm [3]) in the size and the contrast between the MCs and the surrounding breast tissue is not high enough. Figure 1: An example of an MC. (a) A mediolateral-oblique (MLO) mammogram. (b) Expanded view
%U http://www.hindawi.com/journals/jme/2013/615254/