Adaptive feature enhancement for mammographic images with wavelet multiresolution analysis

A novel and computationally efficient approach to an adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis is presented. On wavelet decomposition applied to a given mammographic image, we integrate the information of the tree-structured zero crossings of wavelet coefficients and the information of the low-pass-filtered subimage to enhance the desired image features. A discrete wavelet transform with pyramidal structure is employed to speedup the computation for wavelet decomposition and reconstruction. The spatiofrequency localization property of the wavelet transform is exploited based on the spatial coherence of image and the principle of human psychovisual mechanism. Preliminary results show that the proposed approach is able to adaptively enhance local edge features, suppress noise, and improve global visualization of mammographic image features. This wavelet-based multiresolution analysis is therefore promising for computerized mass screening of mammograms.