Microcalcification detection using fuzzy logic and wavelets

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Micro-calcification clusters (MCCs) are ideal aims for automated detection using image processing and pattern recognition techniques. In this paper, we employ fuzzy logic to handle the fuzziness of mammograms, use Maximum Entropy Principle to determine the fuzzy membership functions, and apply wavelet transform to process the fuzzified images. The 'experimental results have demonstrated that the proposed approach outperforms the approaches using only fuzzy logic or using only wavelet transform.