Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 967
- Type
- D - Journal article
- DOI
-
10.1109/TII.2018.2881343
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 4099
- Volume
- 15
- Issue
- 7
- ISSN
- 1941-0050
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- 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
-
4
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is an output of an international collaboration among researchers at four universities in the UK, China and Australia, supported jointly by eight research grants including 61872241 and 61572316 from the National Natural Science Foundation of China. It proposes an automatic, fast and accurate retinal identification approach using retinal blood vasculature landmarks. The novel approach involves segmenting both thick/thin vessels and then reducing the dimensionality of vessel features. It was validated on 8 public and 1 clinical datasets. The results shows that it achieves an overall segmentation accuracy of 99.65% and the recognition rate of 99.40%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
