A Hybrid the Nonsubsampled Contourlet Transform and Homomorphic Filtering for Enhancing Mammograms

Mammogram is important for early breast cancer detection. But due to the low contrast of microcalcifications and noise, it is difficult to detect microcalcification. This paper presents a comparative study in digital mammography image enhancement based on three different algorithms: homomorphic filtering, unsharp masking and our proposed methods. This latter use a hybrid method Combining contourlet and homomorphic filtering. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBES) and target-to-background enhancement measure based on entropy (TBEE). The proposed methods were tested with the referents mammography data Base MiniMIAS. Experimental results show that the proposed method improves the visibility of microcalcification.