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Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on GeneralizedGaussian Priors

Ningning Zhao 1, 2 Adrian Basarab 2 Denis Kouamé 2 Jean-Yves Tourneret 1
1 IRIT-SC - Signal et Communications
IRIT - Institut de recherche en informatique de Toulouse
2 IRIT-TCI - Traitement et Compréhension d’Images
IRIT - Institut de recherche en informatique de Toulouse
Abstract : This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map, and all the hyperparameters are difficult to be expressed in a closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with the existing approaches via several experiments conducted on realistic synthetic data and in vivo US images.
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Submitted on : Thursday, September 29, 2016 - 4:10:38 PM
Last modification on : Friday, January 29, 2021 - 2:06:18 PM
Long-term archiving on: : Friday, December 30, 2016 - 2:11:59 PM

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Ningning Zhao, Adrian Basarab, Denis Kouamé, Jean-Yves Tourneret. Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on GeneralizedGaussian Priors. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2016, vol. 25 (n° 8), pp. 3736-3750. ⟨10.1109/TIP.2016.2567074⟩. ⟨hal-01374064⟩

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