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Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data

  • François LauzeEmail author
  • Yvain Quéau
  • Esben Plenge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)

Abstract

We propose a variational approach for simultaneous reconstruction and multiclass segmentation of X-ray CT images, with limited field of view and missing data. We propose a simple energy minimisation approach, loosely based on a Bayesian rationale. The resulting non convex problem is solved by alternating reconstruction steps using an iterated relaxed proximal gradient, and a proximal approach for the segmentation. Preliminary results on synthetic data demonstrate the potential of the approach for synchrotron imaging applications.

Keywords

Filter Back Projection Algebraic Reconstruction Technique Discrete Image Inverse Radon Filter Back Projection Reconstruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Aknowledgement

F. Lauze acknowledges funding from the Innovation Fund Denmark and Mærsk Oil and Gas A/S, for the P\(^3\) Project. E. Plenge acknowledges funding from EUs 7FP under the Marie Skodowska-Curie grant (agreement no 600207), and from the Danish Council for Independent Research (grant ID DFF5054-00218).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniverstity of CopenhagenCopenhagenDenmark
  2. 2.Computer Vision GroupTU MünchenMunichGermany

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