Optic disk and cup segmentation through fuzzy broad learning system for glaucoma screening

Riaz Ali, Bin Sheng, Ping Li, Yan Chen, Huating Li, Po Yang, Younhyun Jung, Jinman Kim, C. L. Philip Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2476-2487
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number4
Publication statusPublished - Apr 2021


  • Broad learning system (BLS)
  • fuzzy system
  • neural networks
  • ocular disease
  • optic disk and cup
  • segmentation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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