Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images

Abstract : Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov chain Monte Carlo scheme where the tissue reflectivity function is sampled thanks to a recently introduced proximal unadjusted Langevin algorithm. This new approach is combined with a forward-backward step and a preconditioning strategy to accelerate the convergence, and with a method based on the majorization-minimization principle to solve the inner non-convex minimization problems. As demonstrated in numerical experiments conducted on both simulated and in vivo ultrasound images, the proposed method provides high-quality restoration and segmentation results and is up to six times faster than an existing Hamiltonian Monte Carlo method.
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Contributor : Marie-Caroline Corbineau <>
Submitted on : Tuesday, March 19, 2019 - 5:29:06 PM
Last modification on : Thursday, July 25, 2019 - 4:34:15 PM
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  • HAL Id : hal-02073283, version 1



Marie-Caroline Corbineau, Denis Kouamé, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet. Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images. [Research Report] CVN, CentraleSupélec, Université Paris-Saclay, Gif-Sur-Yvette, France. 2019. ⟨hal-02073283v1⟩



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