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This paper proposes a joint segmentation and deconvolution Bayesian methodfor medical ultrasound (US) images. Contrary to piecewise homogeneous images,US images exhibit heavy characteristic speckle patterns correlated with thetissue structures. The generalized Gaussian distribution (GGD) has been shownto be one of the most relevant distributions for characterizing the speckle inUS images. Thus, we propose a GGD-Potts model defined by a label map couplingUS image segmentation and deconvolution. The Bayesian estimators of the unknownmodel parameters, including the US image, the label map and all thehyperparameters are difficult to be expressed in closed form. Thus, weinvestigate a Gibbs sampler to generate samples distributed according to theposterior of interest. These generated samples are finally used to compute theBayesian estimators of the unknown parameters. The performance of the proposedBayesian model is compared with existing approaches via several experimentsconducted on realistic synthetic data and in vivo US images.

Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors