Hexagonal wavelet processing of digital mammography

Andrew F. Laine; Sergio Schuler; Walter Huda; Janice C. Honeyman-Buck; Barbara G. Steinbach

doi:10.1117/12.154543

14 September 1993 Hexagonal wavelet processing of digital mammography

Andrew F. Laine, Sergio Schuler, Walter Huda, Janice C. Honeyman-Buck, Barbara G. Steinbach

Proceedings Volume 1898, Medical Imaging 1993: Image Processing; (1993) https://doi.org/10.1117/12.154543
Event: Medical Imaging 1993, 1993, Newport Beach, CA, United States

Abstract

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.

Citation Download Citation

Andrew F. Laine, Sergio Schuler, Walter Huda, Janice C. Honeyman-Buck, and Barbara G. Steinbach "Hexagonal wavelet processing of digital mammography", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154543

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