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

Abstract

This 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 guassian distributed (GGD) modeling of subband coefficients. The proposed method is a variant of one of the recently published method BayesShrink by Chang and Vetterli 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 Kσ2/σx where σ and σx are the standard deviation of the noise and the subband data of noise free image respectively, and K is a scale parameter. Experimental results show that proposed method performs better than the Median filter as well as Homomorphic wiener filter especially in terms of feature preservation for better diagnosis as desired in medical image processing.