Abstract
A novel and computationally efficient approach to an adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis is presented. On wavelet de- composition applied to a given mammographic image, we integrate the information of the tree-structured zero crossings of wavelet co- efficients 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 lo- calization property of the wavelet transform is exploited based on the spatial coherence of image and the principle of human psycho- visual mechanism. Preliminary results show that the proposed ap- proach 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. © 1997 SPIE and IS&T. (S1017-9909(97)00704-6)
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