Wavelet Representations for Digital Mammograph
Abstract
This report describes a novel approach for accomplishing mammographic feature analysis by overcomplete multiresolution representations. We show that efficient representations may be identified within a continuum of scale-space and used to enhance features of importance to mammography. We present methods of contrast enhancement based on three overcomplete multiscale representations: (1) the dynamic wavelet transform, (2) the phi-transform, and (3) hexagonal wavelets. Digital mammograms are reconstructed from wavelet coefficients modified at one or more levels by local and global non-linear operators (multiscale edges and histogram modification). In each case, multiscale edges and gain parameters are identified adaptively by a measure of energy within each level of scale-space. We show quantitatively that transform coefficients, modified within each level by adaptive non-linear operators, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology we can improve chances of early detection while requiring less time to evaluate mammograms for most patients. Wavelet analysis, Mammography, Contrast enhancement, Multiscale representations, Digital image processing, Breast cancer, Lab animals, Mice, RAD VI.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 15, 1993
- Accession Number
- ADA275152
Entities
People
- Andrew F. Laine
Organizations
- University of Florida