Speckle Suppression of Ultrasonography Using Maximum Likelihood Estimation and Weighted Nuclear Norm Minimization

Speckle noise corrupts medical ultrasound images and suppression of speckle noise is valuable for image interpretation. This paper presents a new method for speckle suppression named the maximum likelihood based weighted nuclear norm minimization (MLWNNM) filtering by integrating the maximum likelihood estimation (MLE) with the weighted nuclear norm minimization (WNNM). The MLE is first used to get an initially filtered image with reduced Rayleigh distributed noise, and then the WNNM is applied to further improve the denoising effect by preserving and enhancing tissue details. Simulation work shows that when the noise variance is as high as 0.14, the MLWNNM improves the Pratt's figure of merit, peak signal to noise ratio, and mean structural similarity by 123.51%, 0.84%, and 6.13%, respectively, in contrast to the best values of other six methods. Experimental results on clinical ultrasound images suggest that the MLWNNM outperforms other six methods in noise reduction and detail preservation.