Nonlinear filtering enhancement and histogram modeling segmentation of masses for digital mammograms

The objective of this study is to develop an efficient method to highlight the geometric characteristics of defined patterns, and isolate the suspicious regions which in turn provide the improved segmentation of objects. 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 he used as an effective pre-processing step of mass detection with computer scheme