Corbineau, Marie-Caroline and Kouamé, Denis and Chouzenoux, Emilie and Tourneret, Jean-Yves
and Pesquet, Jean-Christophe
Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images.
(2019)
IEEE Signal Processing Letters, 26 (10). 1456-1460. ISSN 1070-9908
|
(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 2MB |
Official URL: http://doi.org/10.1109/LSP.2019.2935610
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.
Repository Staff Only: item control page