Morphological filtering and stochastic modeling-based segmentation of masses on mammographic images
The objective of this study is to develop an efficient method to highlight the geometric characteristics of mass patterns, and isolate the suspicious regions which in turn provide the improved segmentation of suspected masses. In this work, a combined method of using morphological operations, finite generalized Gaussian mixture modeling, and contextual Bayesian relaxation labeling was developed to enhance and segment various mammographic contexts and textures. This method was applied to segment suspicious masses on mammographic images. The testing results showed that the proposed method can detect all suspected masses as well as high contrast objects and can be used as an effective pre-processing step of mass detection in computer-aided diagnosis systems.