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Article Dans Une Revue IEEE Signal Processing Letters Année : 2019

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

Résumé

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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Dates et versions

hal-02073283 , version 1 (19-03-2019)
hal-02073283 , version 2 (14-06-2019)
hal-02073283 , version 3 (01-08-2019)
hal-02073283 , version 4 (21-01-2020)

Identifiants

  • HAL Id : hal-02073283 , version 4

Citer

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, 2019, 26 (10), pp.1456--1460. ⟨hal-02073283v4⟩
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