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Detection of Retinal Blood Vessels Based on Nonlinear Projections

Journal of Signal Processing Systems volume 55, Article number: 103 (2009) Cite this article

Abstract

An automated method for blood vessel segmentation is presented in this paper. The approach uses the nonlinear orthogonal projection to capture the features of vessel networks, and derives a novel local adaptive thresholding algorithm for vessel detection. By embedding in a kind of image decomposition model, the selection of system parameter which reflects the size of concerned convex set is examined. This approach differs from previously known methods in that it uses matched filtering, vessel tracking or supervised methods. The algorithm was tested on two publicly available databases: the DRIVE and the STARE. By comparison with hand-labeled ground truth, an average accuracy of 96.1% is achieved on the former database, and an average accuracy of 90.8% is achieved on the later database.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their comments and suggestions which have greatly improved the presentation of this paper. The authors also thank the authors of DRIVE and STARE databases for making their databases publicly available.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Ningbo University of Technology, Cuibai Road, Ningbo, 315016, China

    Yongping Zhang

  2. Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore, 117543, Singapore

    Yongping Zhang, Wynne Hsu & Mong Li Lee

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  1. Yongping Zhang
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  2. Wynne Hsu
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Correspondence to Yongping Zhang.

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Zhang, Y., Hsu, W. & Lee, M.L. Detection of Retinal Blood Vessels Based on Nonlinear Projections. J Sign Process Syst Sign Image Video Technol 55, 103 (2009). https://doi.org/10.1007/s11265-008-0179-5

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  • DOI: https://doi.org/10.1007/s11265-008-0179-5

Keywords

  • Retinal imaging
  • Vessel detection
  • Nonlinear orthogonal projection
  • Image decomposition
  • Adaptive thresholding
  • Parameter selection