A large margin algorithm for automated segmentation of white matter hyperintensity
- Submitting institution
-
University of Edinburgh
(joint submission with Heriot-Watt University)
- Unit of assessment
- 12 - Engineering
- Output identifier
- 160954336
- Type
- D - Journal article
- DOI
-
10.1016/j.patcog.2017.12.016
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 150
- Volume
- 77
- Issue
- -
- ISSN
- 0031-3203
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- 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
-
7
- Research group(s)
-
C - SSS
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper presents a novel method for automatic cerebral white matter hyperintensity (WMH) segmentation in magnetic resonance imaging. The developed supervised and semi-supervised large margin algorithms for WMH segmentation influenced the work of a number of groups worldwide including that of the internationally leading joint research team from Central South University and Pingdingshan University in China, Virginia Commonwealth University in USA and University of Saskatchewan in Canada (DOI:10.1016/j.media.2020.101791, DOI:10.1016/j.neucom.2019.12.050, DOI:10.1016/j.neucom.2020.05.070, DOI:10.1109/JBHI.2020.3016306).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
