Ultrasound image denoising via maximum a posteriori estimation of wavelet coefficients

Speckle noise removal by means of digital image processors could improve the diagnostic potential of medical ultrasound. This paper addresses the speckle suppression issue within the framework of wavelet analysis. As a first step of our approach, the logarithm of the original image is decomposed into several scales through a multiresolution analysis employing the 2-D wavelet transform. Then, we design a maximum a posteriori (MAP) estimator, which relies on a recently introduced statistical representation for the wavelet coefficients of ultrasound images [A. Achim et. al., 2001]. We use an alpha-stable model to develop a blind noise-removal processor that performs a non-linear operation on the data. Finally, we compare our technique to current state-of-the-art denoising methods applied on actual ultrasound images and we find it more effective, both in terms of speckle reduction and signal detail preservation.