May 2003
Volume 44, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2003
Vessel Analysis in Single Fundus Images Using Functional Imaging
Author Affiliations & Notes
  • A. Fink
    Biomedical Engineering, Technische Universitaet Ilmenau, Ilmenau, Germany
  • C. Kassner
    Biomedical Engineering, Technische Universitaet Ilmenau, Ilmenau, Germany
  • W. Vilser
    Biomedical Engineering, Technische Universitaet Ilmenau, Ilmenau, Germany
  • T. Riemer
    Imedos GmbH, Weimar, Germany
  • E. Nagel
    Outpatient Dept. of Ohthalmology, Rudolstadt, Germany
  • G. Henning
    Outpatient Dept. of Ohthalmology, Rudolstadt, Germany
  • Footnotes
    Commercial Relationships  A. Fink, None; C. Kassner, None; W. Vilser, Imedos F, E, P; T. Riemer, Imedos E; E. Nagel, None; G. Henning, None.
  • Footnotes
    Support  BMBF Germany #13N8001
Investigative Ophthalmology & Visual Science May 2003, Vol.44, 1300. doi:
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    • Get Citation

      A. Fink, C. Kassner, W. Vilser, T. Riemer, E. Nagel, G. Henning; Vessel Analysis in Single Fundus Images Using Functional Imaging . Invest. Ophthalmol. Vis. Sci. 2003;44(13):1300.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Abstract: : Purpose: To facilitate the analysis of retinal vessels in fundus images a functional imaging (FI) was developed. On the base of retinal vessel diameter values the relative segmental vessel resistance index (rel. seg. VRI) was calculated as FI-parameter. The parameter was color coded and displayed assigned to its topological location in the fundus image. In this result image the underlying data are reduced to a clear displayed information which is easy to interpret. As a consequence one can identify critical parts of the retinal network which would be hardly to see in the original image. Methods: The FI was applied on example images acquired during common ophthalmologic examination with a Zeiss FF450 fundus camera, imaging system VisualIS (Imedos). In every image selected vessel sections (vessel parts between two branches) of large retinal vessels were analyzed using the software tool VesselMap (Imedos). With the measured diameters the rel. seg. VRI was calculated as follows: the vessel section was divided into segments of ten microns length. The resistance index of every segment was calculated by one divided by the segment diameter to the power of four. Now the rel. seg. VRI was calculated as the ratio of the segmental resistance index to the mean resistance index of the vessel section in percent. This value was color-coded from red for the highest values to green for the lowest and displayed as an overlay at the assigned vessel location. Results: With the application of the FI on fundus images of patients with diabetes and glaucoma critical parts of the retinal vessel network were instantly detectable. In this parts high resistance values influence the vascular conditions of the retina. Those critical vessel parts would be very hard to identify by common fundus examination (if they would be visible at all). Conclusions: With the FI an evaluation of the retinal vessel network concerning risk factors for vascular dependent diseases and control of the course of a therapy becomes possible. The results of the FI are understandable and easy to interpret not only by specialists but also by general ophthalmologic practitioners. Therefore the FI is a useful tool also in departments doing common fundus examination.

Keywords: imaging/image analysis: clinical • blood supply • retina 
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