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Enhancing joint reconstruction and segmentation with non-convex Bregman iteration

Published version
Peer-reviewed

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Authors

Benning, M 
Gladden, LF 
Mair, R 

Abstract

All imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variational regularisation scheme to a Bregman iteration framework to improve the reconstruction and therefore the segmentation. We develop a novel alternating minimisation scheme that solves the non-convex optimisation problem with provable convergence guarantees. Our results for synthetic and real data show that both reconstruction and segmentation are improved compared to the classical sequential approach.

Description

Keywords

image reconstruction, image segmentation, Bregman iteration, non-convex optimisation, magnetic resonance imaging, total variation, iterative regularisation

Journal Title

Inverse Problems

Conference Name

Journal ISSN

0266-5611
1361-6420

Volume Title

35

Publisher

IOP Publishing
Sponsorship
Engineering and Physical Sciences Research Council (EP/M00483X/1)
Leverhulme Trust (RPG-2015-250)
Engineering and Physical Sciences Research Council (EP/H023348/1)
Engineering and Physical Sciences Research Council (EP/N014588/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
Alan Turing Institute (unknown)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
Engineering and Physical Sciences Research Council (EP/J009539/1)