Bayesian denoising of ultrasound images using heavy‐tailed Levy distribution

Abstract

Ultrasound images often exhibit poor signal to noise ratio when compared with optical images, because of the presence multiplicative speckle noise. Speckle suppression is often carried out as a pre-processing step to aid in diagnosis using ultrasound images. In this study, dual tree complex wavelet transform-based Levy Shrink algorithm is proposed for denoising ultrasound images. The coefficients in each wavelet subband are modelled using a heavy tailed Levy distribution. The scale parameters of the Levy distribution are estimated using fractional moments. Within this framework, a Bayesian estimator is employed to denoise ultrasound images. The proposed Levy Shrink algorithm is verified using evaluation parameters such as peak signal to noise ratio, mean structural similarity index, correlation coefficient and equivalent number of looks. The efficiency of the proposed denoising algorithm is justified by conducting extensive experiments on real as well as simulated ultrasound images.

Quantum-Inspired Weighted Bilateral Filtering for Despeckling in Ultrasound Images

“…This is superior to a simple gradient. Taking into account its eigenvalues and the comparing eigenvectors, the tensor aggregates up the principle directions of the gradient in a predetermined neighborhood of a point, and the extent to which those directions are cognizant [4] .…”

“…ISSN: 2278-0181 http://www.ijert.org be basically accomplished by utilizing the structure tensor, likewise alluded to the second minute grid. For a multivalued picture, the structure tensor has the subsequent structure [4] :…”

“…With ∇ = * ∇ = * ( , ) : the smoothed form of the inclination which is acquired by a convolution with a Gaussian kernel . The structure scale σ decides the extent of the subsequent stream like examples [4] .…”

“…That is, the smooth is bolstered in a homogeneous district and discouraged around limits, so as to protect the discontinuities of the picture. Total Variation (TV) model and the anisotropic smoothing model are the best devices for picture denoising [4] . This paper is organized as follows.…”

Use of Lèvy distribution to analyze longitudinal data with asymmetric distribution and presence of left censored data

“…Stable distributions have been widely used in the analysis of economics and actuarial data sets (Kleiber and Kotz, 2003), in the theoretical and applied physics (Barkai et al, 2000;Tsallis et al, 1995), in genetics research (Yu et al, 2010) and more recent in the modelling of the ultrasound images (Ranjani and Chithra, 2015). Many of these authors, worked with the Lèvy distribution to model the variability of the data in situations with heavy tails and asymmetry; however, we did not find any work that model censored longitudinal data in presence of asymmetry, extreme data and covariates.…”