Medical Image Denoising Using Wavelet Thresholding

In medical images, noise suppression is a particularly delicate and difficult task. A tradeoff between noise reduction and the preservation of actual image features has to be made in a way that enhances the diagnostically relevant image content. The method of wavelet thresholding has been used extensively for denoising medical images. The idea is to transform the data into the wavelet basis, in which the large coefficients are mainly the signal and the smaller ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. In this paper, we evaluate several two-dimensional denoising procedures using medical test images corrupted with additive Gaussian noise. Our results, using the peak-signal-to-noise ratio as a measure of the quality of denoising, show that the NormalShrink method outperforms the other wavelet-based techniques (VisuShrink, BayesShrink). We also demonstrate that garrote shrinkage offers advantages over both hard and soft shrinkage.