Wavelet-based statistical approach for speckle reduction in medical ultrasound images

Abstract

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Ksigma2/sigma(x), where sigma and sigma(x) were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

“…Moreover, we show that our general model fits better to the wavelet coefficients distribution with respect to its counterpart GG model [14,17,18]. • Instead of commonly used bivariate MAP estimator [13][14][15], we introduce a new discrete bivariate MMSE method for estimating denoised coefficients.…”

“…DTCWT offers improved directional selectivity and near shift invariance property. Other medical ultrasound despeckling methods are mainly based on undecimated [17,19] or cycle spinning [12] algorithms in the standard discrete wavelet domain.…”

Artifact removal and texture-based rendering for visualization of 3D fetal ultrasound images

“…Conventionally, this task can be carried out using low pass filters [22]; however, these not only reduce noise but also blur edges. Nowadays, many despeckling methods have been proposed such as adaptive smoothing combined with local statistical parameters [4,11,12], stick techniques [3,5,32], wavelet-based filtering [8,9] and edge-preserving based methods (nonlinear diffusion filters) [3,26,27,34]. Recently, Loizou et al [15] presented an investigation of various kinds of despeckling filters and concluded that filtering is a vital step in ultrasound image analysis.…”

“…Although the quality of surface rendering is more aesthetically pleasing, using splatting techniques generally shifts the boundary location so that the shape is somewhat distorted. In fact, good image quality is essential in visualization, and despeckling (i.e., filtering that removes speckle noise) is helpful to alleviate this situation [3,4,5,8,9,11,12,22,32]. Conventionally, this task can be carried out using low pass filters [22]; however, these not only reduce noise but also blur edges.…”

Enhancement of the ultrasound images by modified anisotropic diffusion method

“…The development of research for anisotropic diffusion via the partial differential equation has taken place in such a way that important structures in the images remain preserved. Apart from anisotropic diffusion methods, several multiscale approaches [8,14,15,26,28,30,32,42,45] were also proposed to reduce speckle in ultrasound images. Most of the recent studies [22,23,27,[43][44][45] on speckle reduction techniques are based on fusion of anisotropic diffusion and multiscale techniques.…”