期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 40, 期 -, 页码 182-193出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2014.10.002
关键词
Optic cup localization; Glaucoma; Model selection; Superpixel classification; Sparse learning
资金
- Singapore A*STAR SERC Grant [092-148-00731]
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. (C) 2014 Elsevier Ltd. All rights reserved.
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