A wavelet based statistical approach for speckle reduction in medical ultrasound images

TLDR: In this article, Chang et al. introduced a novel speckle reduction method based on soft thresholding the wavelet coefficients of the logarithmically transformed medical ultrasound image, which is based on the generalized Gaussian distributed (GGD) modeling of subband coefficients.

Abstract: The paper introduces a novel speckle reduction method based on soft thresholding the wavelet coefficients of the logarithmically transformed medical ultrasound image. The method is based on the generalized Gaussian distributed (GGD) modeling of subband coefficients. The proposed method is a variant of the recently published BayesShrink method (Chang, G et al., IEEE Trans. Image Processing, vol.9, no.9, p.1522-31, 2000) derived in the Bayesian framework for denoising natural images. It is scale adaptive because the parameters required for estimating the threshold depend on scale and subband data. The threshold is computed by K/spl sigma//sup 2///spl sigma//sub x/ where /spl sigma/ and /spl sigma//sub x/ are the standard deviation of the noise and the subband data of the noise-free image, respectively, and K is a scale parameter. Experimental results show that the proposed method performs better than the median filter as well as the homomorphic Wiener filter, especially in terms of feature preservation for better diagnosis as desired in medical image processing.