Skip to Main content Skip to Navigation
Journal articles

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.
Complete list of metadatas

Cited literature [34 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02073283
Contributor : Marie-Caroline Corbineau <>
Submitted on : Tuesday, January 21, 2020 - 7:57:03 PM
Last modification on : Friday, July 31, 2020 - 10:44:09 AM
Document(s) archivé(s) le : Wednesday, April 22, 2020 - 8:13:00 PM

File

SPL_paper_extended.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02073283, version 4

Citation

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-02073283v4⟩

Share

Metrics

Record views

101

Files downloads

55