视盘
计算机视觉
模式识别(心理学)
视杯(胚胎学)
视神经
作者
Riaz Ali,Bin Sheng,Ping Li,Yan Chen,Huating Li,Po Yang,Younhyun Jung,Jinman Kim,C. L. Philip Chen
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2021-04-01
卷期号:17 (4): 2476-2487
被引量:12
标识
DOI:10.1109/tii.2020.3000204
摘要
Glaucoma is an ocular disease that causes permanent blindness if not cured at an early stage. Cup-to-disk ratio (CDR), obtained by dividing the height of optic cup (OC) with the height of optic disk (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. In this article, 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 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.
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