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. red. / Francois Lauze; Yiqiu Dong; Anders Bjorholm Dahl. Springer, 2017. s. 308-319 (Lecture notes in computer science, Bind 10302).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Lauze, FB, Quéau, Y & Plenge, E 2017,
Simultaneous reconstruction and segmentation of CT scans with shadowed data. i F Lauze, Y Dong & AB Dahl (red),
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, bind 10302, s. 308-319, 6th International Conference on Scale Space and Variational Methods in Computer Vision, Kolding, Danmark,
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. I F. Lauze, Y. Dong, & A. B. Dahl (red.),
Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings (s. 308-319). Springer. Lecture notes in computer science Bind 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. I Lauze F, Dong Y, Dahl AB, red., Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. Springer. 2017. s. 308-319. (Lecture notes in computer science, Bind 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. red. / Francois Lauze ; Yiqiu Dong ; Anders Bjorholm Dahl. Springer, 2017. s. 308-319 (Lecture notes in computer science, Bind 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 -
