Registration of Image Sequences from Experimental Low-Cost Fundus Camera

  • Radim Kolar
  • Bernhard Hoeher
  • Jan Odstrcilik
  • Bernhard Schmauss
  • Jiri Jan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8545)


This paper describes new registration approach for registration of low SNR retinal image sequences. We combine two approaches - Fourier-based method for large shift correction and Lucas-Kanade tracking for small shift and rotation correction. We also propose method for evaluation of registration results, which uses spatial variation of minimum value in intensity profiles through blood-vessels. We achieved precision of registration below 2.1 pixels, which is acceptable with regards to image SNR (around 10dB). The final averaging of registered sequence leads to improvement of image quality and improvement in SNR over 10 dB.


Retinal Image Tracking Point Fundus Image Fundus Camera Investigative Ophthalmology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kumar, D.K., Aliahmad, B., Hao, H., Che Azemin, M.Z., Kawasaki, R.: A method for visualization of fine retinal vascular pulsation using nonmydriatic fundus camera synchronized with electrocardiogram. ISRN Ophthalmology 2013, Article ID 865834 (January 2013)Google Scholar
  2. 2.
    Guyomard, J.L., Rosolen, S.G., Paques, M., Delyfer, M.N., Simonutti, M., Tessier, Y., Sahel, J.A., Legargasson, J.F., Picaud, S.: A low-cost and simple imaging technique of the anterior and posterior segments: eye fundus, ciliary bodies, iridocorneal angle. Investigative Ophthalmology & Visual Science 49(11), 5168–5174 (2008)CrossRefGoogle Scholar
  3. 3.
    Tran, K., Mendel, T.A., Holbrook, K.L., Yates, P.A.: Construction of an inexpensive, hand-held fundus camera through modification of a consumer “point-and-shoot” camera. Investigative Ophthalmology & Visual Science 53(12), 7600–7607 (2012)CrossRefGoogle Scholar
  4. 4.
    Haddock, L.J., Kim, D.Y., Mukai, S.: Simple, inexpensive technique for high-quality smartphone fundus photography in human and animal eyes. Journal of Ophthalmology 2013, Article ID 518479, 5 pages (2013)Google Scholar
  5. 5.
    Myung, D., Jais, A., He, L., Blumenkranz, M.S., Chang, R.T.: 3D Printed Smartphone Indirect Lens Adapter for Rapid, High Quality Retinal Imaging. Journal of Mobile Technology in Medicine 3(1), 9–15 (2014)CrossRefGoogle Scholar
  6. 6.
    Ryan, N., Heneghan, C., de Chazal, P.: Registration of digital images using landmark correspondence by expectation maximization. Image and Vision Computing 22, 883–898 (2004)CrossRefGoogle Scholar
  7. 7.
    Deng, K., Tian, J., Zheng, J., Zhang, X., Dai, X., Xu, M.: Retinal Fundus Image Registration via Vascular Structure Graph Matching. International Journal of Biomedical Imaging 2010, Article ID 906067, 13 pages (2010)Google Scholar
  8. 8.
    Ritter, N., Owens, R., Cooper, J., Eikelboom, R.H., Saarloos, P.P.: Registration of Stereo and Temporal Images of the Retina. IEEE Transaction on Medical Imaging 18(5), 404–418 (1999)CrossRefGoogle Scholar
  9. 9.
    Kolar, R., Kubecka, L., Jan, J.: Registration and Fusion of the Autofluorescent and Infrared Retinal Images. International Journal of Biomedical Imaging 2008, Article ID 513478, 11 pages (2008)Google Scholar
  10. 10.
    Chanwimaluang, T., Fan, G., Fransen, S.R.: Hybrid Image Registration. IEEE Transactions on Information Technology in Biomedicine 10(1), 129–142 (2006)CrossRefGoogle Scholar
  11. 11.
    Wang, W., Chen, H., Li, J., Yu, J.: A Registration Method of Fundus Images Based on Edge Detection and Phase-Correlation. In: International Conference on Innovative Computing, Information and Control, vol. 3, pp. 572–576. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  12. 12.
    Kolar, R., Harabis, V., Odstrcilik, J.: Hybrid retinal image registration using phase correlation. The Imaging Science Journal 61(4), 369–384 (2013)CrossRefGoogle Scholar
  13. 13.
    Giancardo, L., Meriaudeau, F., Karnowski, T.P., Tobin, K.W., Grisan, E., Favaro, P., Ruggeri, A., Chaum, E.: Textureless macula swelling detection with multiple retinal fundus images. IEEE Transactions on Biomedical Engineering 58(3), 795–799 (2011)CrossRefGoogle Scholar
  14. 14.
    Li, H., Lu, J., Shi, G., Zhang, Y.: Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm. Biomedical Optics Express 1(1), 31–40 (2010)CrossRefGoogle Scholar
  15. 15.
    Hoeher, B., Voigtmann, P., Michelson, G., Schmauss, B.: Non-mydriatic, wide field, fundus video camera. In: Proc. SPIE 8930, Ophthalmic Technologies XXIV, p. 89300K (February 2014)Google Scholar
  16. 16.
    Pomerantzeff, O., Webb, R., Delori, F.C.: Image formation in fundus cameras. Investigative Ophthalmology and Visual Science 18(6), 630–637 (1979)Google Scholar
  17. 17.
    Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing 39(3), 355–368 (1987)CrossRefGoogle Scholar
  18. 18.
    Bae, J.P., Kim, K.G., Kang, H.C., Jeong, C.B., Park, K.H., Hwang, J.M.: A study on hemorrhage detection using hybrid method in fundus images. Journal of Digital Imaging 24(3), 394–404 (2011)CrossRefGoogle Scholar
  19. 19.
    Kolar, R., Tornow, R.P., Laemmer, R., Odstrcilik, J., Mayer, M.A., Gazarek, J., Jan, J., Kubena, T., Cernosek, P.: Analysis of Visual Appearance of Retinal Nerve Fibers in High Resolution Fundus Images: A Study on Normal Subjects. Computational and Mathematical Methods in Medicine 2013, Article ID 134543, 10 pages (2013)Google Scholar
  20. 20.
    Jan, J.: Digital signal filtering analysis and restoration. IEE Telecommunications Series 44 (2000)Google Scholar
  21. 21.
    Murat, B., Hassan, F.: Subpixel registration directly from the phase difference. EURASIP Journal on Applied Signal Processing 2006, Article ID 60796, 11 pages (2006)Google Scholar
  22. 22.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of Imaging Understanding Workshop, vol. 130, pp. 121–130 (1981)Google Scholar
  23. 23.
    Trujillo, L., Olague, G.: Automated Design of Image Operators that Detect Interest Points. Evolutionary Computation 16(4), 483–507 (2008)CrossRefGoogle Scholar
  24. 24.
    Dowson, N., Bowden, R.: Mutual information for Lucas-Kanade Tracking (MILK): an inverse compositional formulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(1), 180–185 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Radim Kolar
    • 1
    • 2
  • Bernhard Hoeher
    • 3
    • 4
  • Jan Odstrcilik
    • 1
    • 2
  • Bernhard Schmauss
    • 3
    • 4
  • Jiri Jan
    • 1
  1. 1.Department of Biomedical Engineering, Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrnoCzech Republic
  2. 2.International Clinical Research Center, Center of Biomedical EngineeringSt. Anne’s University HospitalBrnoCzech Republic
  3. 3.Institute of Microwaves and PhotonicsFriedrich-Alexander University of Erlangen-NurembergErlangenGermany
  4. 4.Erlangen Graduate School in Advanced Optical Technologies (SAOT)University of Erlangen-NurembergErlangenGermany

Personalised recommendations