  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 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 existing approaches via several experiments
conducted on realistic synthetic data and in vivo US images.
