Ali, R., Sheng, B., Li, P. et al. (6 more authors) (2021) Optic disc and cup segmentation through fuzzy broad learning system for glaucoma screening. IEEE Transactions on Industrial Informatics, 17 (4). pp. 2476-2487. ISSN 1551-3203
Abstract
Glaucoma is an ocular disease that causes permanent blindness if not cured at an early stage. Cup-to-disc ratio (CDR), obtained by dividing the height of optic cup (OC) with the height of optic disc (OD), is a widely adopted metric used for glaucoma screening. Therefore, accurately segmenting OD and OC is crucial for calculating a CDR. Most methods have employed deep learning methods for the segmentation of OD and OC. However, these methods are very time-consuming. We present a new fuzzy broad learning system-based technique for OD and OC segmentation with glaucoma screening. We comprehensively integrated extracting a region of interest (ROI) from RGB images, data augmentation, extracting red and green channel images, and inputting them to the two separate fuzzy broad learning system-based neural networks for segmenting the OD and OC, respectively, and then calculated CDR. Experiments show that our fuzzy broad learning system-based technique outperforms many state-of-the-art methods.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Ocular disease; optic disc and cup; segmentation; broad learning system; neural networks; fuzzy system |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jun 2020 14:51 |
Last Modified: | 15 Nov 2021 17:43 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TII.2020.3000204 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161479 |