Robust Automatic Montaging of Adaptive Optics Flood Illumination Retinal Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)


Adaptive optics (AO) flood illumination camera acquires retinal images with a limited field of view, which can be extended by image alignment into one wide field of view montage image. The image alignment into a montage requires efficient and accurate image registration. Since manual registration is demanding and disadvantageous, automatic registration is a beneficial improvement. We propose the first fully automated AO retinal image montage procedure. Here, we present three novel fully automated registration methods, which are based on two established image processing approaches. The first method utilizes scale invariant feature transform (SIFT) in combination with specific image preprocessing. The second method uses the phase correlation (PC) approach and the last method is a connection of PC and SIFT (PC-SIFT) algorithm. In total, 200 images acquired from the left and right eyes of 10 subjects were used for creating the wide field-of-view montage images and compared with manual montaging. The automated image montage was successfully achieved. Alignment accuracy evaluated by normalized mutual information metric showed that the PC-SIFT approach established the most accurate results, these are higher than manual montaging. Therefore, the AO montaging registration methods are able to achieve promising results in accuracy and time demand in comparison with manual montaging. Hence, the latter can be replaced by those fully automated procedures.


Retina Adaptive optics Flood illumination SIFT Phase correlation Image montaging 



The authors express their sincere gratitude to Imagine Eyes, Orsay, France, for continuous support and loan of the rtx1e instrument for the measurements of this study.


  1. 1.
    Can, A., Stewart, C., Roysam, B., Tanenbaum, H.: A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 347–364 (2002). Scholar
  2. 2.
    Carroll, J., Neitz, M., Hofer, H., Neitz, J., Williams, D.R.: Functional photoreceptor loss revealed with adaptive optics. Proc. Natl. Acad. Sci. 101(22), 8461–8466 (2004). Scholar
  3. 3.
    Chen, M., Cooper, R.F., Gee, J.C., Brainard, D.H., Morgan, J.I.W.: Automatic longitudinal montaging of adaptive optics retinal images using constellation matching. Biomed. Opt. Express 10(12), 6476–6496 (2019). Scholar
  4. 4.
    Chen, M., Cooper, R.F., Han, G.K., Gee, J., Brainard, D.H., Morgan, J.I.W.: Multi-modal automatic montaging of adaptive optics retinal images. Biomed. Opt. Express 7(12), 4899–4918 (2016). Scholar
  5. 5.
    Chew, A.L., Sampson, D.M., Kashani, I., Chen, F.K.: Agreement in cone density derived from gaze-directed single images versus wide-field montage using adaptive optics flood illumination ophthalmoscopy. Transl. Vision Sci. Technol 6(6), 1–13 (2017). Scholar
  6. 6.
    Georgiou, M., Kalitzeos, A., Patterson, E.J., Dubra, A., Carroll, J., Michaelides, M.: Adaptive optics imaging of inherited retinal diseases. Brit. J. Ophthalmol. 102(8), 1028–1035 (2018). Scholar
  7. 7.
    Gill, J.S., Moosajee, M., Dubis, A.M.: Cellular imaging of inherited retinal diseases using adaptive optics. Eye 33(11), 1683–1698 (2019). Scholar
  8. 8.
    Kuglin, C.D.: Performance of the phase correlator in image guidance applications. Technical report, Control Data Corp Minneapolis MN Image Systems DIV (1976)Google Scholar
  9. 9.
    Li, H., Lu, J., Shi, G., Zhang, Y.: Automatic montage of retinal images in adaptive optics confocal scanning laser ophthalmoscope. Opt. Eng. 51(5), 1–6 (2012). Scholar
  10. 10.
    Liang, J., Williams, D.R., Miller, D.T.: Supernormal vision and high-resolution retinal imaging through adaptive optics. J. Opt. Soc. Am. A 14(11), 2884–2892 (1997). Scholar
  11. 11.
    Lombardo, M., Serrao, S., Ducoli, P., Lombardo, G.: Eccentricity dependent changes of density, spacing and packing arrangement of parafoveal cones. Ophthal. Physiol. Opt. 33(4), 516–526 (2013). Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999).
  13. 13.
    Paques, M., et al.: Adaptive optics ophthalmoscopy. Prog. Retinal EyeRes. 66(1), 1–16 (2018).,
  14. 14.
    Prasse, M., Rauscher, F.G., Wiedemann, P., Reichenbach, A., Francke, M.: Optical properties of retinal tissue and the potential of adaptive optics to visualize retinal ganglion cells in vivo. Cell Tissue Res. 353(2), 269–278 (2013). Scholar
  15. 15.
    Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3(Dec), 583–617 (2002)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Valterova, E.: Automatic adaptive optics retinal images montaging. In: Proceedings of the 27th Conference STUDENT EEICT 2021. Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií, Brno (2021)Google Scholar
  17. 17.
    Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008).
  18. 18.
    Williams, D.R.: Imaging single cells in the living retina. Vision Res. 51(13), 1379–1396 (2011).,
  19. 19.
    Xue, B., Choi, S.S., Doble, N., Werner, J.S.: Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera. J. Opt. Soc. Am. A 24(5), 1364–1372 (2007). Scholar

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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Institute for Medical Informatics, Statistics, and EpidemiologyLeipzig UniversityLeipzigGermany
  2. 2.Faculty of Electrical Engineering and Communications, Department of Biomedical EngineeringBrno University of TechnologyBrnoCzech Republic

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