Breast tissue density classification using wavelet-based texture descriptors

Abstract

It has been well established that the risk of breast cancer development is associated with increased breast density. Therefore, characterization of breast tissue density is clinically significant. In the present work, the potential of various wavelet energy descriptors (derived from ten different compact support wavelet filters) has been investigated for breast tissue density classification using kNN, SVM, and PNN classifiers. The work has been carried out on the MIAS dataset. The highest classification accuracy of 96.2% is achieved using the kNN classifier Haar wavelet energy descriptors.