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Proceedings Paper

Adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis
Author(s): Lulin Chen; Chang Wen Chen; Kevin J. Parker
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Paper Abstract

This paper presents a novel and computationally efficient approach to an adaptive mammographic image feature enhancement using wavelet-based multiresolution analysis. Upon wavelet decomposition applied to a given mammographic image, we integrate the information of the tree-structured zerocrossings 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 has been employed to speed up the computation for wavelet decomposition and reconstruction. The spatio-frequency 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.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); doi: 10.1117/12.236013
Show Author Affiliations
Lulin Chen, Univ. of Rochester (United States)
Chang Wen Chen, Univ. of Rochester (United States)
Kevin J. Parker, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 2762:
Wavelet Applications III
Harold H. Szu, Editor(s)

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