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3D Retinal Vascular Network from Optical Coherence Tomography Data

  • Pedro Guimarães
  • Pedro Rodrigues
  • Pedro Serranho
  • Rui Bernardes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

The retinal vascular network is directly observable by non-invasive techniques, and changes of its status have been associated to retinal and cardiac pathologies. In order to infer on these changes, studies have been performed using 2D fundus images. However, measurements such as vessel tortuosity or bifurcation angle suffer from missing depth information.

In this work we aim to consider the retinal vascular network in 3D as imaged by optical coherence tomography (OCT). We take advantage of proprietary software developed by our research group able to segment the vascular network from OCT fundus reference images (personal communication). This approach allows for the comparison between vessel and non-vessel A-scans and thus to highlight differences such as the hyper-reflectivity and the shadows casted by vessels.

Keywords

Biomedical Imaging Optical Coherence Tomography 3D Image Analysis Vascular Network Segmentation Retina 

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References

  1. 1.
    Witt, N., Wong, T.Y., Hughes, A.D., Chaturvedi, N., Klein, B.E., Evans, R., McNamara, M., Thom, S.A., Klein, R.: Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke. Hypertension 47, 975–981 (2006)CrossRefGoogle Scholar
  2. 2.
    Kwa, V.I., van der Sande, J.J., Stam, J., Tijmes, N., Vrooland, J.L.: Retinal arterial changes correlate with cerebral small-vessel disease. Neurology 59, 1536–1540 (2002)CrossRefGoogle Scholar
  3. 3.
    Drexler, W., Fujimoto, J.G.: Optical Coherence Tomography: Technology and Applications. Springer (2008)Google Scholar
  4. 4.
    Niemeijer, M., Garvin, M., van Ginnekan, B., Sonka, M., Abràmoff, M.: Vessel Segmentation in 3D Spectral OCT Scans of the Retina. In: Proc. SPIE, vol. 6914, p. 69141R-1-8 (2008), doi:10.1117/12.772680Google Scholar
  5. 5.
    Salem, N., Salem, S., Nandi, A.: Segmentation of retinal blood vessels based on analysis of the Hessian Matrix and Clustering Algorithm. In: 15th European Signal Processing Conference, pp. 428–432 (2007)Google Scholar
  6. 6.
    Lee, T.: Image Representation Using 2D Gabor Wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 959–971 (1996)CrossRefGoogle Scholar
  7. 7.
    Kovesi, P.: Image Features from Phase Congruency. Videre: A Journal of Computer Vision Research 1(3) (1999)Google Scholar
  8. 8.
    Kovesi, P.: Symmetry and Asymmetry from Local Phase. In: Proc. Tenth Australian Joint Conference on Artificial Intelligence, pp. 185–190 (1997)Google Scholar
  9. 9.
    Orfanidis, S.J.: Introduction to Signal Processing. Prentice-Hall, Englewood Cliffs (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pedro Guimarães
    • 1
  • Pedro Rodrigues
    • 2
  • Pedro Serranho
    • 1
    • 3
  • Rui Bernardes
    • 1
    • 2
  1. 1.IBILI, Faculty of MedicineUniversity of CoimbraCoimbraPortugal
  2. 2.AIBILI/CNTM, Association for Innovation and Biomedical Research on Light, and ImageCoimbraPortugal
  3. 3.Mathematics Section, Department of Science and TechnologyOpen UniversityLisbonPortugal

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