Comparison of CAD Systems for Three Class Breast Tissue Density Classification Using Mammographic Images

It is well known that the changes in the breast tissue density are strongly correlated with the risk of breast cancer development and therefore classifying the breast tissue density as fatty, fatty–glandular and dense–glandular has become clinically significant. It is believed that the changes in the tissue density can be captured by computing the texture descriptors. Accordingly, the present work has been carried out with an aim to explore the potential of Laws’ mask texture descriptors for description of variations in breast tissue density using mammographic images. The work has been carried out on the 322 mammograms taken from the MIAS dataset. The dataset consists of 106 fatty, 104 fatty–glandular and 112 dense–glandular images. The ROIs of size 200 × 200 pixels are extracted from the center of the breast tissue, ignoring the pectoral muscle. For the design of a computer aided diagnostic system for three class breast tissue density classification, Laws’ texture descriptors have been computed using Laws’ masks of different resolutions. Five statistical features i.e. mean, skewness, standard deviation, entropy and kurtosis have been computed from all the Laws’ texture energy images generated from each ROI. The feature space dimensionality reduction has been carried out by using principal component analysis. For the classification task kNN, PNN and SVM classifiers have been used. After carrying out exhaustive experimentation, it has been observed that PCA–SVM based CAD system design yields the highest overall classification accuracy of 87.5 %, with individual class accuracy values of 84.9, 84.6 and 92.8 % for fatty, fatty–glandular and dense–glandular image classes respectively. These results indicate the usefulness of the proposed CAD system for breast tissue density classification.