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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editor: Gregory W. Faris
  • Vol. 2, Iss. 6 — Jun. 13, 2007

Texton-based segmentation of retinal vessels

Donald A. Adjeroh, Umasankar Kandaswamy, and J. Vernon Odom  »View Author Affiliations

JOSA A, Vol. 24, Issue 5, pp. 1384-1393 (2007)

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With improvements in fundus imaging technology and the increasing use of digital images in screening and diagnosis, the issue of automated analysis of retinal images is gaining more serious attention. We consider the problem of retinal vessel segmentation, a key issue in automated analysis of digital fundus images. We propose a texture-based vessel segmentation algorithm based on the notion of textons. Using a weak statistical learning approach, we construct textons for retinal vasculature by designing filters that are specifically tuned to the structural and photometric properties of retinal vessels. We evaluate the performance of the proposed approach using a standard database of retinal images. On the DRIVE data set, the proposed method produced an average performance of 0.9568 specificity at 0.7346 sensitivity. This compares well with the best-published results on the data set 0.9773 specificity at 0.7194 sensitivity [ Proc. SPIE 5370, 648 (2004) ].

© 2007 Optical Society of America

OCIS Codes
(170.3880) Medical optics and biotechnology : Medical and biological imaging

ToC Category:
Image Processing

Original Manuscript: August 10, 2006
Manuscript Accepted: September 28, 2006
Published: April 11, 2007

Virtual Issues
Vol. 2, Iss. 6 Virtual Journal for Biomedical Optics

Donald A. Adjeroh, Umasankar Kandaswamy, and J. Vernon Odom, "Texton-based segmentation of retinal vessels," J. Opt. Soc. Am. A 24, 1384-1393 (2007)

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