Spectral correlation analysis for microcalcification detection in digital mammogram images

Breast cancer has been threatening lives of women around the world. Thus, Computer Aided Diagnosis (CAD) systems play an important role in early detection of breast cancer. In this study, we propose a CAD system based on cyclostationary signal analysis for microcalcifications detection. Spectral correlation is estimated for regions of interests (ROIs) after conversion to 1D vector. The proposed algorithm utilizes simple statistical features calculation for the raw spectral data followed by student-t test for evaluation and reduction of the generated set of features. Support Vector Machines (SVM) with linear kernel was employed for the classification task. The experimental results showed that the proposed approach is superior compared to several state of the art methods. This approach was tested using digital database for screening mammogram (DDSM) and it achieved sensitivity, specificity and accuracy of 95.88%, 93.10% and 94.44% respectively.