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Abstract #26001 Published in IGR 12-2
Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI
Wong DW; Liu J; Lim JH; Tan NM; Zhang Z; Lu S; Li H; Teo MH; Chan KL; Wong TYConference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2009; 2009: 5777-5780
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.
D.W. Wong. Institute for Infocomm Research, ASTAR, Singapore. wkwong@i2r.a-star.edu.sg
Classification:
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
6.9.1.1 Confocal Scanning Laser Ophthalmoscopy (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.1 Laser scanning)
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
6.8.2 Posterior segment (Part of: 6 Clinical examination methods > 6.8 Photography)
