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.
Wavelet analysis is currently being investigated as an image enhancement tool for use in mammography. Although this approach to image processing appears to have great promise, there remain major uncertainties regarding an optimal form of wavelet based algorithms. It is, therefore, desirable to have a quantitative method for evaluating a wavelet based image processing algorithm. Optimizationofalgorithmspriorto evaluationusingstand&dReceiver Operating Characteristic (ROC) methods is made possible. A mathematical method has been developed where the input signal is a gaussian with added random noise. An enhancement factor (EF) is obtained from input and output signal-to-noise ratios, SNR and NRQ, (EF = NRQ I SNR). The development and testing of this method is described, and a practical application is given showing the major features of a wavelet based image processing algorithm based on the Frazier-Jawerth transform.
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