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
- Annu Int Conf Ieee Eng Med Biol Soc
Volume / Issue
- 2009 /
Start / End Page
- 5777 - 5780
PubMed ID
- 19963657
Pubmed Central ID
- 19963657
International Standard Serial Number (ISSN)
- 2375-7477
Digital Object Identifier (DOI)
- 10.1109/IEMBS.2009.5332534
Language
- eng
Conference Location
- United States