Performance Analysis of Morphological Component Analysis (MCA) Method for Mammograms Using Some Statistical Features

摘要

Early detection of breast cancer helps reducing the mortality rates. Mammography is very useful tool in breast cancer detection. But it is very difficult to separate different morphological features in mammographic images. In this study, Morphological Component Analysis (MCA) method is used to extract different morphological aspects of mammographic images by effectively preserving the morphological characteristics of regions. MCA decomposes the mammogram into piecewise smooth part and the texture part using the Local Discrete Cosine Transform (LDCT) and Curvelet Transform via wrapping (CURVwrap). In this study, simple comparison in performance has been done using some statistical features for the original image versus the piecewise smooth part obtained from the MCA decomposition. The results show that MCA suppresses the structural noises and blood vessels from the mammogram and enhances the performance for mass detection.