LAUSR

Breast density is known as a significant indicator of breast cancer risk prediction and greatly reduces the digital mammograms sensitivity. In this work, based on the simple pulse coupled neural network (SPCNN), a novel Morph_SPCNN model is proposed for dealing with the limitations of over-segmentation that commonly existed in density segmentation of mammograms. To evaluate the proposed model, the segmentation result is employed as a feature map of the breast density classification system. In addtion, the texture features of mammogram calculated based on the gray level co-occurrence matrix (GLCM) and the statistical features (mean, skewness, kurtosis) are extracted and input to the support vector machine (SVM) for breast density classification. Finally, the performance of SVM classifier is evaluated based on the ten-fold cross-validation. Our method is verified both on the MIAS dataset, DDSM database and hybrid dataset (MIAS database and Gansu Provincial Academy of Medical Sciences (GPAMS) database), respectively achieving 87.80%, 94.89% and 95.37% accuracy for breast density classification. The experimental results indicate that our proposed method has greatly improved the performance of breast density segmentation and classification.