Extending Diabetic Retinopathy Imaging from Color to Spectra

  • Pauli Fält
  • Jouni Hiltunen
  • Markku Hauta-Kasari
  • Iiris Sorri
  • Valentina Kalesnykiene
  • Hannu Uusitalo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

In this study, spectral images of 66 human retinas were collected. These spectral images were measured in vivo from 54 voluntary diabetic patients and 12 control subjects using a modified ophthalmic fundus camera system. This system incorporates the optics of a standard fundus microscope, 30 narrow bandpass interference filters ranging from 400 to 700 nanometers at 10 nm intervals, a steady-state broadband lightsource and a monochrome digital charge-coupled device camera. The introduced spectral fundus image database will be expanded in the future with professional annotations and will be made public.

Keywords

Spectral image human retina ocular fundus camera interference filter retinopathy diabetes mellitus 

References

  1. 1.
    DRIVE: Digital Retinal Images for Vessel Extraction, http://www.isi.uu.nl/Research/Databases/DRIVE/
  2. 2.
    Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23, 501–509 (2004)CrossRefGoogle Scholar
  3. 3.
    STARE: STructured Analysis of the Retina, http://www.parl.clemson.edu/stare/
  4. 4.
    Kauppi, T., Kalesnykiene, V., Kämäräinen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kälviäinen, H., Pietilä, J.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the 11th Conference on Medical Image Understanding and Analysis (MIUA 2007), pp. 61–65 (2007)Google Scholar
  5. 5.
    Delori, F.C., Burns, S.A.: Fundus reflectance and the measurement of crystalline lens density. J. Opt. Soc. Am. A 13, 215–226 (1996)CrossRefGoogle Scholar
  6. 6.
    Savage, G.L., Johnson, C.A., Howard, D.L.: A comparison of noninvasive objective and subjective measurements of the optical density of human ocular media. Optom. Vis. Sci. 78, 386–395 (2001)CrossRefGoogle Scholar
  7. 7.
    Delori, F.C.: Spectrophotometer for noninvasive measurement of intrinsic fluorescence and reflectance of the ocular fundus. Appl. Opt. 33, 7439–7452 (1994)CrossRefGoogle Scholar
  8. 8.
    Van Norren, D., Tiemeijer, L.F.: Spectral reflectance of the human eye. Vision Res. 26, 313–320 (1986)CrossRefGoogle Scholar
  9. 9.
    Delori, F.C., Pflibsen, K.P.: Spectral reflectance of the human ocular fundus. Appl. Opt. 28, 1061–1077 (1989)CrossRefGoogle Scholar
  10. 10.
    Bone, R.A., Brener, B., Gibert, J.C.: Macular pigment, photopigments, and melanin: Distributions in young subjects determined by four-wavelength reflectometry. Vision Res. 47, 3259–3268 (2007)CrossRefGoogle Scholar
  11. 11.
    Beach, J.M., Schwenzer, K.J., Srinivas, S., Kim, D., Tiedeman, J.S.: Oximetry of retinal vessels by dual-wavelength imaging: calibration and influence of pigmentation. J. Appl. Physiol. 86, 748–758 (1999)Google Scholar
  12. 12.
    Ramella-Roman, J.C., Mathews, S.A., Kandimalla, H., Nabili, A., Duncan, D.D., D’Anna, S.A., Shah, S.M., Nguyen, Q.D.: Measurement of oxygen saturation in the retina with a spectroscopic sensitive multi aperture camera. Opt. Express 16, 6170–6182 (2008)CrossRefGoogle Scholar
  13. 13.
    Khoobehi, B., Beach, J.M., Kawano, H.: Hyperspectral Imaging for Measurement of Oxygen Saturation in the Optic Nerve Head. Invest. Ophthalmol. Vis. Sci. 45, 1464–1472 (2004)Google Scholar
  14. 14.
    Hirohara, Y., Okawa, Y., Mihashi, T., Amaguchi, T., Nakazawa, N., Tsuruga, Y., Aoki, H., Maeda, N., Uchida, I., Fujikado, T.: Validity of Retinal Oxygen Saturation Analysis: Hyperspectral Imaging in Visible Wavelength with Fundus Camera and Liquid Crystal Wavelength Tunable Filter. Opt. Rev. 14, 151–158 (2007)CrossRefGoogle Scholar
  15. 15.
    Hammer, M., Thamm, E., Schweitzer, D.: A simple algorithm for in vivo ocular fundus oximetry compensating for non-haemoglobin absorption and scattering. Phys. Med. Biol. 47, N233–N238 (2002)CrossRefGoogle Scholar
  16. 16.
    Styles, I.B., Calcagni, A., Claridge, E., Orihuela-Espina, F., Gibson, J.M.: Quantitative analysis of multi-spectral fundus images. Med. Image Anal. 10, 578–597 (2006)CrossRefGoogle Scholar
  17. 17.
    Johnson, W.R., Wilson, D.W., Fink, W., Humayun, M., Bearman, G.: Snapshot hyperspectral imaging in ophthalmology. J. Biomed. Opt. 12, 014036 (2007)CrossRefGoogle Scholar
  18. 18.
    Stewart, C.V., Tsai, C.-L., Roysam, B.: The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Trans. Med. Imag. 22, 1379–1394 (2003)CrossRefGoogle Scholar
  19. 19.
    Yang, G., Stewart, C.V., Sofka, M., Tsai, C.-L.: Registration of challenging image pairs: initialization, estimation, and decision. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1973–1989 (2007)CrossRefGoogle Scholar
  20. 20.
    MATLAB: MATrix LABoratory, The MathWorks, Inc., http://www.mathworks.com/matlab
  21. 21.
    Gaillard, E.R., Zheng, L., Merriam, J.C., Dillon, J.: Age-related changes in the absorption characteristics of the primate lens. Invest. Ophthalmol. Vis. Sci. 41, 1454–1459 (2000)Google Scholar
  22. 22.
    Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd edn. John Wiley & Sons, Inc., New York (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pauli Fält
    • 1
  • Jouni Hiltunen
    • 1
  • Markku Hauta-Kasari
    • 1
  • Iiris Sorri
    • 2
  • Valentina Kalesnykiene
    • 2
  • Hannu Uusitalo
    • 2
    • 3
  1. 1.InFotonics Center Joensuu, Department of Computer Science and StatisticsUniversity of JoensuuJoensuuFinland
  2. 2.Department of OphthalmologyKuopio University Hospital and University of KuopioKuopioFinland
  3. 3.Department of OphthalmologyTampere University HospitalTampereFinland

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