Non-decimated 2D Wavelet Spectrum and Its Use in Breast Cancer Diagnostics

TL;DR

This paper introduces a non-decimated wavelet transform technique that enhances scaling parameter estimation and improves breast cancer diagnostic accuracy using mammogram analysis.

Contribution

The paper presents a novel NDWT-based method for better scaling estimation that outperforms traditional wavelet approaches in breast cancer diagnostics.

Findings

Improved estimation of the Hurst exponent with lower mean-squared errors.

Diagnostic accuracy for mammogram classification exceeds 80%.

NDWT method outperforms conventional orthogonal wavelet methods.

Abstract

To improve diagnostic accuracy of breast cancer detection, several researchers have used the wavelet-based tools, which provide additional insight and information for aiding diagnostic decisions. The accuracy of such diagnoses, however, can be improved. This paper introduces a wavelet-based technique, non-decimated wavelet transform (NDWT)-based scaling estimation, that improves scaling parameter estimation over the traditional methods. One distinctive feature of NDWT is that it does not decimate wavelet coefficients at multiscale levels resulting in redundant outputs which are used to lower the variance of scaling estimators. Another interesting feature of the proposed methodology is the freedom of dyadic constraints for inputs, typical for standard wavelet-based approaches. To compare the estimation performance of the NDWT method to a conventional orthogonal wavelet transform-based method, we use simulation to estimate the Hurst exponent in two-dimensional fractional Brownian fields. The results of the simulation show that the proposed method improves the conventional estimators of scaling and yields estimators with smaller mean-squared errors. We apply the NDWT method to classification of mammograms as cancer or control and, for publicly available mammogram images from the database at the University of South Florida, find the the diagnostic accuracy in excess of 80%.