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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 adopt- ing 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 noncon- vex minimization problems. As demonstrated in numerical ex- periments 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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Submitted on : Tuesday, February 11, 2020 - 2:48:11 PM
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Marie-Caroline Corbineau, Denis Kouamé, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet. Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2019, 26 (10), pp.1456-1460. ⟨10.1109/LSP.2019.2935610⟩. ⟨hal-02474585⟩



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