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

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 : Thursday, August 1, 2019 - 6:10:44 PM
Last modification on : Friday, July 31, 2020 - 10:44:09 AM
Document(s) archivé(s) le : Wednesday, January 8, 2020 - 3:02:26 PM


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  • HAL Id : hal-02073283, version 3


Marie-Caroline Corbineau, Denis Kouamé, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet. Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images - Extended Version. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2019, 26 (10), pp.1456--1460. ⟨hal-02073283v3⟩



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