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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Optic Cup Segmentation for Glaucoma Detection Using Low-Rank Superpixel Representation
verfasst von : Yanwu Xu, Lixin Duan, Stephen Lin, Xiangyu Chen, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
Verlag: Springer International Publishing
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We present an unsupervised approach to segment optic cups in fundus images for glaucoma detection without using any additional training images. Our approach follows the superpixel framework and domain prior recently proposed in [1], where the superpixel classification task is formulated as a low-rank representation (LRR) problem with an efficient closed-form solution. Moreover, we also develop an adaptive strategy for automatically choosing the only parameter in LRR and obtaining the final result for each image. Evaluated on the popular
ORIGA
dataset, the results show that our approach achieves better performance compared with existing techniques.