A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5
- Type
- D - Journal article
- DOI
-
10.1016/j.cmpb.2018.03.015
- Title of journal
- Computer Methods and Programs in Biomedicine
- Article number
- -
- First page
- 11
- Volume
- 160
- Issue
- -
- ISSN
- 0169-2607
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S0169260717310398?via%3Dihub
- 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
-
6
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work presents novel imaging technology for the quantitative analysis of endothelial cell abnormalities in corneal confocal microscopy images. This paper is co-authored with Manchester Royal Eye Hospital, Weill Cornell Medical College and University of Manchester and initially funded by the NHS National Innovation Centre. This work is significant because it provides objective method for identifying corneal endothelial cell abnormalities associated with a number of eye and systemic diseases. This work forms part of the PhD thesis of Al-Fahdawi 2018 (Supervisor: Qahwaji). It led to further joint publication (https://tinyurl.com/yzmbxrfn) and Qahwaji invited to join IET’s Healthcare Sector Executive Committee (https://tinyurl.com/yzcz9qcl).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
