Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors
This work addresses the challenge of analyzing ultrasound images with speckle patterns for medical imaging applications, representing an incremental advancement in domain-specific techniques.
The paper tackles the problem of joint segmentation and deconvolution in medical ultrasound images by proposing a hierarchical Bayesian model based on generalized Gaussian priors, achieving performance improvements compared to existing methods as validated on synthetic and in vivo data.
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.