Stochastic model and probabilistic decision-based classifier for mass detection in digital mammography

In this paper, we have developed a combined method utilizing morphological operations, a finite generalized Gaussian mixture (FGGM) modeling, and a contextual Bayesian relaxation labeling technique (CBRL) to enhance and extract suspicious masses. A feature space is constructed based on multiple feature extraction from the regions of interest (ROIs). Finally, a multimodular probabilistic decision-based classifier is employed to distinguish true masses from non-masses.