Mammographic Images Enhancement and Denoising for Microcalbfication Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. In this paper, we present an algorithm for mammographic images enhancement and denoising based on the wavelet transform. In particular, we develop an adaptive procedure to perform an optimal denoising using a local iterative fuzzy noise variance estimation. Moreover, the degree of enhancement is adoptively tuned at each scale. The proposed algorithm has been tested on clinical images