A new feature extraction method based on multi-resolution representations of mammograms

Display Omitted This paper presents an approach for breast cancer diagnosis in digital mammograms using shearlet transform.A new approach is represented for feature extraction using shearlet transform, t-test statistic and dynamic thresholding.The system uses two datasets (ROIs) obtained from two different database; MIAS and DDSM database.The datasets include all abnormalites for breast cancer.The classification section was performed to distinguish the ROIs as normal-abnormal and benign-malignant. In this paper, I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform. Initially, fast finite shearlet transform was performed over mammogram images, and feature vectors were built using coefficients of the transform. In subsequent calculations, features were ranked according to t-test statistics, and capabilities were distinguished between different classes. To maximize differences between class representatives, a thresholding process was implemented as a final stage of feature extraction, and classifications were calculated over the optimal feature set using 5-fold cross validation and a support vector machine (SVM) classifier. The present results show that the proposed method provides satisfactory classification accuracy.