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Optic Cup Segmentation for Glaucoma Detection Using Low-Rank Superpixel Representation

  • Yanwu Xu
  • Lixin Duan
  • Stephen Lin
  • Xiangyu Chen
  • Damon Wing Kee Wong
  • Tien Yin Wong
  • Jiang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

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.

Keywords

Singular Value Decomposition Fundus Image Supervise Method Unsupervised Approach Glaucoma Diagnosis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yanwu Xu
    • 1
  • Lixin Duan
    • 1
  • Stephen Lin
    • 2
  • Xiangyu Chen
    • 1
  • Damon Wing Kee Wong
    • 1
  • Tien Yin Wong
    • 3
  • Jiang Liu
    • 1
  1. 1.Institute for Infocomm ResearchAgency for Science, Technology and ResearchSingapore
  2. 2.Microsoft ResearchP.R. China
  3. 3.Department of OphthalmologyNational University of SingaporeSingapore

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