A new algorithm for mammograms enhancement and denoising based on multiscale geometric analysis (MGA) was proposed. Firstly, mammograms were decomposed into different scales and directional subbands by using nonsubsampled contourlet transform (NSCT). After modeling the coefficients of each directional subbands and using generalized gaussian mixture model (GGMM) according to the statistical property of the transform coefficients, they were categorized into strong edges, weak edges and noise by Bayesian classifier. A nonlinear mapping function was designed to enhance and suppress the different coefficients adaptively so as to obtain a good enhancement result with significant features according to the property of every subband coefficient. Finally, the mammograms were obtained by reconstructing the original maps with the modified coefficients of NSCT. Experimental results illustrate that the proposed approach is practicable and robust.