Standard
Simultaneous reconstruction and segmentation of CT scans with shadowed data. / Lauze, Francois Bernard; Quéau, Yvain; Plenge, Esben.
Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. ed. / Francois Lauze; Yiqiu Dong; Anders Bjorholm Dahl. Springer, 2017. p. 308-319 (Lecture notes in computer science, Vol. 10302).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Lauze, FB, Quéau, Y & Plenge, E 2017,
Simultaneous reconstruction and segmentation of CT scans with shadowed data. in F Lauze, Y Dong & AB Dahl (eds),
Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. Springer, Lecture notes in computer science, vol. 10302, pp. 308-319, 6th International Conference on Scale Space and Variational Methods in Computer Vision, Kolding, Denmark,
04/06/2017.
https://doi.org/10.1007/978-3-319-58771-4_25
APA
Lauze, F. B., Quéau, Y., & Plenge, E. (2017).
Simultaneous reconstruction and segmentation of CT scans with shadowed data. In F. Lauze, Y. Dong, & A. B. Dahl (Eds.),
Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings (pp. 308-319). Springer. Lecture notes in computer science Vol. 10302
https://doi.org/10.1007/978-3-319-58771-4_25
Vancouver
Lauze FB, Quéau Y, Plenge E.
Simultaneous reconstruction and segmentation of CT scans with shadowed data. In Lauze F, Dong Y, Dahl AB, editors, Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. Springer. 2017. p. 308-319. (Lecture notes in computer science, Vol. 10302).
https://doi.org/10.1007/978-3-319-58771-4_25
Author
Lauze, Francois Bernard ; Quéau, Yvain ; Plenge, Esben. / Simultaneous reconstruction and segmentation of CT scans with shadowed data. Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. editor / Francois Lauze ; Yiqiu Dong ; Anders Bjorholm Dahl. Springer, 2017. pp. 308-319 (Lecture notes in computer science, Vol. 10302).
Bibtex
@inproceedings{976a33009c054daa8a5f0f75d30a6783,
title = "Simultaneous reconstruction and segmentation of CT scans with shadowed data",
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.",
author = "Lauze, {Francois Bernard} and Yvain Qu{\'e}au and Esben Plenge",
year = "2017",
month = jun,
doi = "10.1007/978-3-319-58771-4_25",
language = "English",
isbn = "978-3-319-58770-7",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "308--319",
editor = "Francois Lauze and Yiqiu Dong and Dahl, {Anders Bjorholm}",
booktitle = "Scale Space and Variational Methods in Computer Vision",
address = "Switzerland",
note = "null ; Conference date: 04-06-2017 Through 08-06-2017",
}
RIS
TY - GEN
T1 - Simultaneous reconstruction and segmentation of CT scans with shadowed data
AU - Lauze, Francois Bernard
AU - Quéau, Yvain
AU - Plenge, Esben
N1 - Conference code: 6
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-58771-4_25
DO - 10.1007/978-3-319-58771-4_25
M3 - Article in proceedings
SN - 978-3-319-58770-7
T3 - Lecture notes in computer science
SP - 308
EP - 319
BT - Scale Space and Variational Methods in Computer Vision
A2 - Lauze, Francois
A2 - Dong, Yiqiu
A2 - Dahl, Anders Bjorholm
PB - Springer
Y2 - 4 June 2017 through 8 June 2017
ER -
