Retinal Layer Segmentation in Optical Coherence Tomography Images
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 083-210486-6415
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2947761
- Title of journal
- Ieee Access
- Article number
- -
- First page
- 152388
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8871107
- 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
-
3
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this work, the fuzzy C-means and fuzzy histogram hyperbolisation methods are integrated into the continuous max-flow graph-cut framework. Also, the unique hyper-reflective features of OCT retina structures are modelled into the graph-cut based process. Experimental results have proved that this approach is an efficient and effective way to handle inhomogeneity. Extended work from an award winning conference paper in Bioimging 2018 and furthered collaboration with a world-leading eye hospital (Tongren Eye Hospital, China).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
