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Spectrally optimal illuminations for diabetic retinopathy detection in retinal imaging

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Abstract

Retinal photography is a standard method for recording retinal diseases for subsequent analysis and diagnosis. However, the currently used white light or red-free retinal imaging does not necessarily provide the best possible visibility of different types of retinal lesions, important when developing diagnostic tools for handheld devices, such as smartphones. Using specifically designed illumination, the visibility and contrast of retinal lesions could be improved. In this study, spectrally optimal illuminations for diabetic retinopathy lesion visualization are implemented using a spectrally tunable light source based on digital micromirror device. The applicability of this method was tested in vivo by taking retinal monochrome images from the eyes of five diabetic volunteers and two non-diabetic control subjects. For comparison to existing methods, we evaluated the contrast of retinal images taken with our method and red-free illumination. The preliminary results show that the use of optimal illuminations improved the contrast of diabetic lesions in retinal images by 30–70%, compared to the traditional red-free illumination imaging.

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Acknowledgements

The authors would like to thank the Academy of Finland for funding (ReVision project, Funding Decision No. 259530). The strategic funding from the Faculty of Science and Forestry, University of Eastern Finland and Elsemay Börn Fund are also acknowledged. The authors would like to thank Elina Hietanen, for support and assistance with the imaging.

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Authors and Affiliations

  1. School of Computing and Institute of Photonics, University of Eastern Finland, P.O. Box 111, FI-80101, Joensuu, Finland

    Piotr Bartczak, Pauli Fält & Markku Hauta-Kasari

  2. Focus Action Ltd., P.O. Box 71, FI-33471, Ylöjärvi, Finland

    Niko Penttinen

  3. Department of Ophthalmology, the SILK Research and Development Center for Ophthalmic Innovations, School of Medicine, University of Tampere, FI-33014, Tampere, Finland

    Pasi Ylitepsa & Hannu Uusitalo

  4. Tays Eye Center, Tampere, Finland

    Hannu Uusitalo

  5. Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, P.O. Box 20, FI-53851, Lappeenranta, Finland

    Lauri Laaksonen & Lasse Lensu

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  1. Piotr Bartczak
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  2. Pauli Fält
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  3. Niko Penttinen
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  8. Hannu Uusitalo
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Correspondence to Piotr Bartczak.

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Bartczak, P., Fält, P., Penttinen, N. et al. Spectrally optimal illuminations for diabetic retinopathy detection in retinal imaging. Opt Rev 24, 105–116 (2017). https://doi.org/10.1007/s10043-016-0300-0

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