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We developed and implemented two locally adaptive image smoothing filters to improve the signal to noise ratio of digitized mammogram images. The application of these smoothing filters in conjunction with the deconvolution of the images results in better visualization of image details. Previous efforts in restoration of digitized mammograms have assumed a stationary image with uncorrelated white Gaussian noise. In this work we considered a more realistic case of a non-stationary image model and signal-dependent noise of photonic and film-grain origins. Both the camera blur and the MTF of the screen-film combination were considered. The camera noise may be minimized through averaging and background subtraction. The signal-dependent nature of the radiographic noise was modelled by a linear shift-invariant system and the relative strengths of various noise sources were compared. The deconvolution filter was designed to respond to the particular form of the noise in the system based on the Minimum Mean Squared Error (MMSE) criteria. Of the two smoothing filters the Bayesian estimator was found to outperform the adaptive Wiener filter. Filters were implemented in a real time processing environment using our mammographic image acquisition and analysis system.
Aghdasi et al. (Thu,) studied this question.