Mammograms Enhancement and Denoising Using Generalized Gaussian Mixture Model in Nonsubsampled Contourlet Transform

Abstract

In this paper, a novel algorithm for mammographic images enhancement and denoising based on Multiscale Geometric Analysis (MGA) is proposed. Firstly mammograms are decomposed into different scales and directional subbands using Nonsubsampled Contourlet Transform (NSCT). After modeling the coefficients of each directional subbands using Generalized Gaussian Mixture Model (GGMM) according to the statistical property, they are categorized into strong edges, weak edges and noise by Bayesian classifier. To enhance the suspicious lesion and suppress the noise, a nonlinear mapping function is designed to adjust the coefficients adaptively so as to obtain a good enhancement result with significant features. Finally, the resulted mammographic images are obtained by reconstructing with the modified coefficients using NSCT. Experimental results illustrate that the proposed approach is practicable and robustness, which outperforms the spatial filters and other methods based on wavelets in terms of mass and microcalcification denoising and enhancement.