Information fusion approach to microcalcification characterization

The paper presents an information fusion-based approach to one of the most challenging problems in mammogram interpretation: the problem of characterizing mammographic microcalcifications as benign or malignant. There are two categories of methods typically used for designing decision aids for diagnosis of microcalcifications: computer vision methods employing intensity-based features automatically extracted from images and methods using mammogram characteristics considered by human experts. The achieved recognition accuracy of both types of methods is not yet sufficient for them to be utilized in clinical practice. The paper introduces a hybrid system combining decisions of classifiers utilizing both domain knowledge-based and intensity-based features within the framework of the Evidence theory. The system comprises a hierarchical evidential classifier employing a combination of texture features of individual microcalcifications and a neural network employing cluster features observed and described by a radiologist. The results of a pilot study have shown that a false alarm rate of the hybrid system is lower than the false alarm rate of each single classifier used in the combination as well as that of the radiologists participated in the study.