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Abstract #59621 Published in IGR 16-3
Robust multi-scale superpixel classification for optic cup localization
Tan NM; Xu Y; Goh WB; Liu JComputerized Medical Imaging and Graphics 2015; 40: 182-193
This paper presents an optimal model integration framework to robustly localize the optic cup in fundus images for glaucoma detection. This work is based on the existing superpixel classification approach and makes two major contributions. First, it addresses the issues of classification performance variations due to repeated random selection of training samples, and offers a better localization solution. Second, multiple superpixel resolutions are integrated and unified for better cup boundary adherence. Compared to the state-of-the-art intra-image learning approach, we demonstrate improvements in optic cup localization accuracy with full cup-to-disc ratio range, while incurring only minor increase in computing cost.
iMED Ocular Programme, Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore; School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore. Electronic address: nmtan@i2r.a-star.edu.sg.
Full articleClassification:
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
