  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%.
