Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis

  • Yanwu Xu
  • Dong Xu
  • Stephen Lin
  • Jiang Liu
  • Jun Cheng
  • Carol Y. Cheung
  • Tin Aung
  • Tien Yin Wong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

We propose a machine learning framework based on sliding windows for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary structural image cue for clinically identifying glaucoma. This localization uses a bundle of sliding windows of different sizes to obtain cup candidates in each disc image, then extracts from each sliding window a new histogram based feature that is learned using a group sparsity constraint. An ε-SVR (support vector regression) model based on non-linear radial basis function (RBF) kernels is used to rank each candidate, and final decisions are made with a non-maximal suppression (NMS) method. Tested on the large ORIGA − light clinical dataset, the proposed method achieves a 73.2% overlap ratio with manually-labeled ground-truth and a 0.091 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. The high accuracy of this framework on images from low-cost and widespread digital fundus cameras indicates much promise for developing practical automated/assisted glaucoma diagnosis systems.

Keywords

Feature Selection Radial Basis Function Support Vector Regression Optic Nerve Head Color Channel 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yanwu Xu
    • 1
  • Dong Xu
    • 1
  • Stephen Lin
    • 2
  • Jiang Liu
    • 3
  • Jun Cheng
    • 3
  • Carol Y. Cheung
    • 4
  • Tin Aung
    • 4
    • 5
  • Tien Yin Wong
    • 4
    • 5
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Microsoft Research AsiaP.R. China
  3. 3.Institute for Infocomm ResearchAgency for Science, Technology and ResearchSingapore
  4. 4.Singapore Eye Research InstituteSingapore
  5. 5.Department of OphthalmologyNational University of SingaporeSingapore

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