Restoration of Ultrasound Images Using A Hierarchical Bayesian Model with A Generalized Gaussian Prior - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

Restoration of Ultrasound Images Using A Hierarchical Bayesian Model with A Generalized Gaussian Prior

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Abstract

This paper addresses the problem of ultrasound image restoration within a Bayesian framework. The distribution of the ultrasound image is assumed to be a generalized Gaussian distribution (GGD). The main contribution of this work is to propose a hierarchical Bayesian model for estimating the GGD parameters. The Bayesian estimators associated with this model are difficult to be expressed in closed form. Thus we investigate a Markov chain Monte Carlo method which is used to generate samples asymptotically distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the GGD parameters. The performance of the proposed Bayesian model is tested with synthetic data and compared with the performance obtained with the expectation maximization algorithm.
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Dates and versions

hal-01399870 , version 1 (21-11-2016)

Identifiers

  • HAL Id : hal-01399870 , version 1
  • OATAO : 15170

Cite

Ningning Zhao, Adrian Basarab, Denis Kouamé, Jean-Yves Tourneret. Restoration of Ultrasound Images Using A Hierarchical Bayesian Model with A Generalized Gaussian Prior. IEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. pp. 4577-4581. ⟨hal-01399870⟩
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