Robust multi-scale superpixel classification for optic cup localization

Robust multi-scale superpixel classification for optic cup localization
Title:
Robust multi-scale superpixel classification for optic cup localization
Other Titles:
Computerized Medical Imaging and Graphics
DOI:
10.1016/j.compmedimag.2014.10.002
Keywords:
Publication Date:
10 October 2014
Citation:
: Tan N-M, et al. Robust multi-scale superpixel classification for optic cup localization. Comput Med Imaging Graph (2014), http://dx.doi.org/10.1016/j.compmedimag.2014.10.002
Abstract:
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.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
0895-6111
1879-0771
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