Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI.

Published

Journal Article

Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.

Full Text

Duke Authors

Cited Authors

  • Wong, DWK; Liu, J; Lim, JH; Tan, NM; Zhang, Z; Lu, S; Li, H; Teo, MH; Chan, KL; Wong, TY

Published Date

  • 2009

Published In

Volume / Issue

  • 2009 /

Start / End Page

  • 5777 - 5780

PubMed ID

  • 19963657

Pubmed Central ID

  • 19963657

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/IEMBS.2009.5332534

Language

  • eng

Conference Location

  • United States