A Bayesian Approach for the Classification of Mammographic Masses

Breast cancer is a major cause of deaths among women and the leading cause of death among all cancers for middle-aged women in most developed countries. Presently there are no methods to prevent breast cancer thus early detection of this disease represents a very important factor in its treatment and plays a major role in reducing mortality. Mammography is one of the most reliable methods in early detection of breast cancer. In this paper, we present a novel algorithm for medical mammogram image classification, based on the Dirichlet mixture model. Our method can be divided into three main steps: Preprocessing, feature extraction, and image classification. First, histogram equalization is used to remove the noise and to enhance the quality of the image. Later, we extract texture information from mammographic images using the Local Binary Pattern (LBP) and Haralick texture descriptor (HTD). Then, we use the Birth and Death Markov Chain Monte Carlo to estimate the parameters of the Dirichlet mixture representing each class from our training set. Finally, in the classification stage, each mammogram image is assigned to the class increasing more its likelihood. Extensive simulations are used to show the merits of our approach.