Weighted level set evolution based on local edge features for medical image segmentation
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6100
- Type
- D - Journal article
- DOI
-
10.1109/TIP.2017.2666042
- Title of journal
- IEEE Transactions on Image Processing
- Article number
- -
- First page
- 1979
- Volume
- 26
- Issue
- 4
- ISSN
- 1057-7149
- Open access status
- Compliant
- Month of publication
- April
- 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
- No
- Number of additional authors
-
2
- Research group(s)
-
A - Applied Computing
- Citation count
- 70
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the top journal in image processing, this highly-cited paper presents a novel level-set optimisation technique that significantly enhances the segmentation of medical images using Kimmel’s active contours. This research, which was co-funded by EPSRC and a Marie Curie Career Integration Grant, has impacted on image segmentation (Wang, Tsinghua University), edge detection (Shui, National Laboratory of Radar Signal Processing), and face recognition (Liu, Chongqing University of Posts and Telecommunications). This work has also informed a new line of research on local feature extraction for image segmentation (Han, Nanjing University of Aeronautics and Astronautics).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -