Denoising and enhancing digital mammographic images for visual screening

Abstract

Dense regions in digital mammographic images are usually noisy and have low contrast, and their visual screening is difficult. This paper describes a new method for mammographic image noise suppression and enhancement, which can be effective particularly for screening image dense regions. Initially, the image is preprocessed to improve its local contrast and the discrimination of subtle details. Next, image noise suppression and edge enhancement are performed based on the wavelet transform. At each resolution, coefficients associated with noise are modelled by Gaussian random variables; coefficients associated with edges are modelled by Generalized Laplacian random variables, and a shrinkage function is assembled based on posterior probabilities. The shrinkage functions at consecutive scales are combined, and then applied to the wavelets coefficients. Given a resolution of analysis, the image denoising process is adaptive (i.e. does not require further parameter adjustments), and the selection of a gain factor provides the desired detail enhancement. The enhancement function was designed to avoid introducing artifacts in the enhancement process, which is essential in mammographic image analysis. Our preliminary results indicate that our method allows to enhance local contrast, and detect microcalcifications and other suspicious structures in situations where their detection would be difficult otherwise. Compared to other approaches, our method requires less parameter adjustments by the user.