Medical image classification using birth-and-death MCMC

Breast cancer is one of the main causes of death among American women. The use of screening mammography is widely recommended for early diagnosis of breast cancer. In this paper, we propose a highly efficient algorithm for medical mammogram image classification, based on the generalized Beta mixture model. The proposed method, first extracts texture information from mammographic images then model it using the generalized Beta mixture models. For classification, we use the Earth Mover Distance (EMD) metric. Our work is motivated by the fact that mammographic images contain non-Gaussian texture characteristics, impossible to model using rigid distributions like the Gaussian. Experimental results are provided to show the merits of the proposed approach.