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International Journal of Biomedical Imaging
Volume 2013 (2013), Article ID 154860, 11 pages
Research Article

Robust Vessel Segmentation in Fundus Images

A. Budai,1,2,3 R. Bock,1,3 A. Maier,1,3 J. Hornegger,1,3 and G. Michelson3,4,5

1Pattern Recognition Lab, Friedrich-Alexander University, Erlangen-Nuremberg, 91058 Erlangen, Germany
2International Max Planck Research School for Optics and Imaging (IMPRS), 91058 Erlangen, Germany
3Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, Germany
4Department of Ophthalmology, Friedrich-Alexander University, Erlangen-Nuremberg, 91058 Erlangen, Germany
5Interdisciplinary Center of Ophthalmic Preventive Medicine and Imaging (IZPI), 91054 Erlangen, Germany

Received 4 June 2013; Revised 18 September 2013; Accepted 21 September 2013

Academic Editor: Yue Wang

Copyright © 2013 A. Budai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.