Simulated phantom images for optimizing wavelet-based image processing algorithms in mammography

摘要

Image processing techniques using wavelet signal analysis have shown some promise in mammography. It is desirable, however, to optimize these algorithms before subjecting them to clinical evaluation. In this study, computer simulated images were used to study the significance of all the parameters available in a multiscale wavelet image processing algorithm designed to enhance mammograms. Computer simulated images had a gaussian-shaped signal in half of the regions of interest and included added random noise. Signal intensity and noise levels were varied to determine the detection threshold contrast-to-noise ratio (CNR). An index of the ratio of output to input contrast to noise ratios was used to optimize a wavelet based image processing algorithm. Computed CNRs were generally found to correlate well with signal detection by human observers in both the original and processed images. Use of simulated phantom images enabled the parameters associated with multiscale wavelet based processing techniques to be optimized.