Microcalcification texture analysis in a hybrid system for computer-aided mammography

Characterization of microcalcifications with high level of confidence is a very challenging problem since microcalcifications are very small and the difference between benign and malignant clusters is often very subtle. The overall goal of the presented research is to develop a hybrid evidential system for characterization of microcalcifications in order to provide radiologists with a computerized decision aid. The hybrid system intelligently combines a domain knowledge based subsystem with a computer vision subsystem to improve the confidence level of microcalcification characterization. This paper is mainly devoted to the description of the developed computer vision part of the hybrid system. The computer vision subsystem is represented by a hierarchical evidential classifier that computes evidences about the class membership of individual microcalcifications based on their texture and then uses these evidences in a neural network for clusters characterization. The texture of each individual classification is represented by two features: the fractal dimension and a four dimension vector defined by coefficients of the Gabor expansion of a microcalcification image. The results obtained in our experiment prove the feasibility of using this method in the hybrid system.