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To improve diagnostic accuracy of breast cancer detection, severalresearchers have used the wavelet-based tools, which provide additional insightand information for aiding diagnostic decisions. The accuracy of suchdiagnoses, however, can be improved. This paper introduces a wavelet-basedtechnique, non-decimated wavelet transform (NDWT)-based scaling estimation,that improves scaling parameter estimation over the traditional methods. Onedistinctive feature of NDWT is that it does not decimate wavelet coefficientsat multiscale levels resulting in redundant outputs which are used to lower thevariance of scaling estimators. Another interesting feature of the proposedmethodology is the freedom of dyadic constraints for inputs, typical forstandard wavelet-based approaches. To compare the estimation performance of theNDWT method to a conventional orthogonal wavelet transform-based method, we usesimulation to estimate the Hurst exponent in two-dimensional fractionalBrownian fields. The results of the simulation show that the proposed methodimproves the conventional estimators of scaling and yields estimators withsmaller mean-squared errors. We apply the NDWT method to classification ofmammograms as cancer or control and, for publicly available mammogram imagesfrom the database at the University of South Florida, find the the diagnosticaccuracy in excess of 80%.

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