An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration.
- Submitting institution
-
University of Oxford
- Unit of assessment
- 12 - Engineering
- Output identifier
- 15447
- Type
- D - Journal article
- DOI
-
10.1016/j.media.2014.05.005
- Title of journal
- Medical image analysis
- Article number
- 8
- First page
- 1299
- Volume
- 18
- Issue
- 8
- ISSN
- 1361-8415
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
https://www.dir-lab.com/
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work described in this paper won the best paper award at Medical Image Computing and Computed Assisted Intervention conference (the premiere international conference), and subsequently was invited for a journal publication. This work is amongst first works on automated lung motion estimation that did not require manual lung annotations to provide accurate alignment between consecutive acquisition. This method has been used to analyse lung CT from clinical trials in Oxford Cancer Imaging Centre (funded by CRUK&EPSRC, NS/A000024/1), and a follow-up research was investigated under CNRS-Oxford Collaboration funded by John Fell OUP Research Fund (DFD07920).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -