Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-09-1343
- Type
- D - Journal article
- DOI
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10.1016/j.bspc.2017.02.012
- Title of journal
- Biomedical Signal Processing and Control
- Article number
- -
- First page
- 50
- Volume
- 35
- Issue
- -
- ISSN
- 1746-8094
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- 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
-
-
- Research group(s)
-
-
- Citation count
- 24
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a retinal fundus image exudate detection algorithm that relies upon the inclusion of contextual clues to strengthen the performance of the ensemble classifier of bootstrapped decision trees. The exhaustive testing of this approach demonstrates its robustness across four different public datasets.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -