MAMMOGRAM SCREENING USING MULTIRESOLUTION-BASED IMAGE SEGMENTATION

Abstract

This paper describes the design, implementation, and testing of an adaptive digital image segmentation method that detects cancerous changes in mammograms and can potentially aid medical experts in establishing the diagnosis. The essence of the method is hierarchical region growing that uses pyramidal multiresolution image representation. The relationships between pixels at different resolution levels are established using a fuzzy membership function, thus enabling detection of very small and/or low contrast objects in a highly textured background. The selection of the parameters of the fuzzy membership function allows for fine-tuning the method to specific segmentation objectives. This paper discusses two versions of the method: the first is aimed at the detection of microcalcifications and the second at the detection of benign and malignant nodules. The two versions are fully automated and differ in the procedure applied to automatically select the appropriate parameters of the fuzzy membership function. Both versions were evaluated in two ways: (i) using synthetically generated objects superimposed on normal mammograms and (ii) using mammogram images for which the corresponding truth images were generated by human experts. The objective of the first evaluation was to precisely determine the method’s capabilities and its sensitivity to object size, shape, and contrast. The objective of the second evaluation was to establish the method’s usefulness in helping medical experts to establish the diagnosis.