Application of statistical texture features for breast tissue density classification

Abstract

It has been strongly advocated that increase in density of breast tissue is strongly correlated with the risk of developing breast cancer. Accordingly change in breast tissue density pattern is taken seriously by radiologists. In typical cases, the breast tissue density patterns can be easily classified into fatty, fatty-glandular and dense glandular classes, but the differential diagnosis between atypical breast tissue density patterns from mammographic images is a daunting challenge even for the experienced radiologists due to overlap of the appearances of the density patterns. Therefore a CAD system for the classification of the different breast tissue density patterns from mammographic images is highly desirable. Accordingly in the present work, exhaustive experiments have been carried out to evaluate the performance of statistical features using PCA-kNN, PCA-PNN, PCA-SVM and PCA-SSVM based CAD system designs for two-class and three-class breast tissue density classification using mammographic images. It is observed that for two-class breast tissue density classification, the highest classification accuracy of 94.4%is achieved using only the first 10 principal components (PCs) derived from statistical features with the SSVM classifier. For three-class breast tissue density classification, the highest classification accuracy of 86.3% is achieved using only the first 4 PCs with SVM classifier.