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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.

Open Access
Online
ISSN
2364-5504
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A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

Caterina Rust
  • Corresponding author
  • Institute of Mathematics and Image Computing, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
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/ Stephanie Häger
  • Institute of Mathematics and Image Computing, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
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/ Nadine Traulsen / Jan Modersitzki
  • Institute of Mathematics and Image Computing, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
  • Fraunhofer Institute for Medical Image Computing MEVIS, Maria-Goeppert-Straße 3, 23562 Lübeck, Germany
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Published Online: 2017-09-07 | DOI: https://doi.org/10.1515/cdbme-2017-0113

Abstract

Accurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.

Keywords: optic disc segmentation; fovea detection

About the article

Published Online: 2017-09-07


Citation Information: Current Directions in Biomedical Engineering, Volume 3, Issue 2, Pages 533–537, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2017-0113.

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©2017 Caterina Rust et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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