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Curvelet-Based Bayesian Estimator for Speckle Suppression in Ultrasound Imaging

  • Rafat DamsehEmail author
  • M. Omair Ahmad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)

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.

Keywords

Ultrasound imaging Curvelet transform Speckle noise Bayesian estimation Statistical modeling 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institut de génie biomédicalÉcole Polytechnique de MontréalMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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