Curvelet-Based Bayesian Estimator for Speckle Suppression in Ultrasound Imaging

Abstract

Ultrasound images are inherently affected by speckle noise, and thus reducing this noise is crucial for successful post-processing. One powerful approach for noise suppression in digital images is Bayesian estimation. In the Bayesian-based despeckling schemes, the choice of suitable statistical models and the development of a shrinkage function for estimation of the noise-free signal are the major concerns. In this paper, a novel curvelet-based Bayesian estimator for speckle removal in ultrasound images is developed. The curvelet coefficients of the degradation model of the noisy ultrasound image are decomposed into two components, namely noise-free signal and signal-dependent noise. The Cauchy and two-sided exponential distributions are assumed to be statistical models for the two components, respectively, and an efficient low-complexity realization of the Bayesian estimator is proposed. The experimental results demonstrate the superiority of the proposed despeckling scheme in achieving significant speckle suppression and preserving image details.

This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and in part by the Regroupement Strategique en Microelectronique du Quebec (ReSMiQ).