Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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). https://doi.org/10.1109/34.990136
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). https://doi.org/10.1073/pnas.0401440101
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). https://doi.org/10.1364/BOE.10.006476
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). https://doi.org/10.1364/BOE.7.004899
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). https://doi.org/10.1167/tvst.6.6.9
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). https://doi.org/10.1136/bjophthalmol-2017-311328
Gill, J.S., Moosajee, M., Dubis, A.M.: Cellular imaging of inherited retinal diseases using adaptive optics. Eye 33(11), 1683–1698 (2019). https://doi.org/10.1038/s41433-019-0474-3
Kuglin, C.D.: Performance of the phase correlator in image guidance applications. Technical report, Control Data Corp Minneapolis MN Image Systems DIV (1976)
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). https://doi.org/10.1117/1.OE.51.5.057008
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). https://doi.org/10.1364/JOSAA.14.002884
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). https://doi.org/10.1111/opo.12053
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). https://doi.org/10.1109/ICCV.1999.790410
Paques, M., et al.: Adaptive optics ophthalmoscopy. Prog. Retinal EyeRes. 66(1), 1–16 (2018). https://doi.org/10.1016/j.preteyeres.2018.07.001, https://linkinghub.elsevier.com/retrieve/pii/S1350946217300782
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). https://doi.org/10.1007/s00441-013-1602-1
Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3(Dec), 583–617 (2002)
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)
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008). http://www.vlfeat.org/
Williams, D.R.: Imaging single cells in the living retina. Vision Res. 51(13), 1379–1396 (2011). https://doi.org/10.1016/j.visres.2011.05.002, https://linkinghub.elsevier.com/retrieve/pii/S0042698911001763
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). https://doi.org/10.1364/JOSAA.24.001364
Acknowledgment
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Valterova, E., Rauscher, F.G., Kolar, R. (2021). Robust Automatic Montaging of Adaptive Optics Flood Illumination Retinal Images. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds) Medical Image Understanding and Analysis. MIUA 2021. Lecture Notes in Computer Science(), vol 12722. Springer, Cham. https://doi.org/10.1007/978-3-030-80432-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-80432-9_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80431-2
Online ISBN: 978-3-030-80432-9
eBook Packages: Computer ScienceComputer Science (R0)