Mammographic lesion classification using discrete orthonormal s-transform

Abstract

Breast cancer is the leading cause of cancer in women. Early detection of breast cancer through periodic screening improves the chances of recovery. However, the small and subtle signs of the early disease make the task of accurate diagnosis particularly arduous for radiologists. Computer aided diagnosis of the mammographic images is currently very popular as it helps radiologists classify lesions as normal or abnormal, benign or malignant. This thesis presents an efficient mammographic lesion classification approach for the detection of breast cancer. The approach uses the two dimensional discrete orthonormal S-transform (DOST) method to extract the coefficients from the digital mammograms. A feature selection algorithm based on statistical two-sample t-test method is used for the selection of significant coefficients from the high dimensional DOST coefficients. The selected significant coefficients are used as features for the classification of mammographic lesions as benign or malignant. This scheme utilizes a back propagation neural network as the classifier. The scheme is validated using MIAS database. The result shows an optimal classification accuracy rate of and a performance index value of AUC = in receiver operating characteristic (ROC) curve. These results are very promising in comparison with existing discrete wavelet transform (DWT).