Simultaneous reconstruction and segmentation of CT scans with shadowed data

Francois Bernard Lauze, Yvain Quéau, Esben Plenge

7 Citationer (Scopus)

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.
OriginalsprogEngelsk
TitelScale Space and Variational Methods in Computer Vision : 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings
RedaktørerFrancois Lauze, Yiqiu Dong, Anders Bjorholm Dahl
Antal sider12
ForlagSpringer
Publikationsdatojun. 2017
Sider308-319
ISBN (Trykt)978-3-319-58770-7
ISBN (Elektronisk)978-3-319-58771-4
DOI
StatusUdgivet - jun. 2017
Begivenhed6th International Conference on Scale Space and Variational Methods in Computer Vision - Kolding, Danmark
Varighed: 4 jun. 20178 jun. 2017
Konferencens nummer: 6

Konference

Konference6th International Conference on Scale Space and Variational Methods in Computer Vision
Nummer6
Land/OmrådeDanmark
ByKolding
Periode04/06/201708/06/2017
NavnLecture notes in computer science
Vol/bind10302
ISSN0302-9743

Citationsformater