Retinal Image Registration Based on Bifurcation Point and SURF

  • Haiying XiaEmail author
  • Danhua Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)


Retinal image registration is the process of matching and superimposing two retinal images of the same patient. The traditional feature-based retinal image registration algorithm is computationally expensive during the matching process. This paper proposes a fast and efficient registration method based on the combination of bifurcation point and SURF algorithm. First, the eight-neighbor search algorithm is used to detect the bifurcation points of the reference image and the target image, and then the SURF feature is extracted in the rectangular template region centered on the bifurcation point. The Euclidean distance is used to perform rough matching on the extracted features, then RANSAC is used for fine matching, and finally the transformation model is estimated. Experiments show that this method can quickly and effectively achieve the registration of retinal images while reducing a large number of unnecessary searches and achieving a great registration result.


Retinal image registration Bifurcation point SURF Feature extraction RANSAC 



This work is supported by the National Natural Science Foundation of China (No. 61762014). The project was funded by a major project of Guangxi Science and Technology (Guike AA18118009).


  1. 1.
    Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. 24(4), 325–376 (1992)CrossRefGoogle Scholar
  2. 2.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  3. 3.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22(8), 986–1004 (2003)CrossRefGoogle Scholar
  4. 4.
    Cideciyan, A.V.: Registration of ocular fundus images. IEEE Eng. Med. Biol. Mag. 14, 52–58 (1995)CrossRefGoogle Scholar
  5. 5.
    Zana, F., Klein, J.C.: A multimodal registration algorithm of eye fundus images using vessels detection and hough transform. IEEE Trans. Med. Imaging 18(5), 419–428 (1999)CrossRefGoogle Scholar
  6. 6.
    Laliberte, F., Gagnon, L., Sheng, Y.: Registration and fusion of retinal images-an evaluation study. IEEE Trans. Med. Imaging 22(5), 661–673 (2003)CrossRefGoogle Scholar
  7. 7.
    Fang, B., Tang, Y.Y.: Elastic registration for retinal images based on reconstructed vascular trees. IEEE Trans. Biomed. Eng. 53(6), 1183–1187 (2006)CrossRefGoogle Scholar
  8. 8.
    Chen, L., Huang, X., Tian, J.: Retinal image registration using topological vascular tree segmentation and bifurcation structures. Biomed. Signal Process. Control 16, 22–31 (2015)CrossRefGoogle Scholar
  9. 9.
    Besl, P.J., Mckay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  10. 10.
    Stewart, C.V., Tsai, C.L., Roysam, B.: The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Trans. Med. Imaging 22(11), 1379–1394 (2003)CrossRefGoogle Scholar
  11. 11.
    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(11), 1973–1989 (2007)CrossRefGoogle Scholar
  12. 12.
    Chen, J., Tian, J., Lee, N., Zheng, J., Smith, R.T., Laine, A.F.: A partial intensity invariant feature descriptor for multimodal retinal image registration. IEEE Trans. Biomed. Eng. 57(7), 1707–1718 (2010)CrossRefGoogle Scholar
  13. 13.
    Wang, G., Wang, Z., Chen, Y., Zhao, W.: Robust point matching method for multimodal retinal image registration. Biomed. Signal Process. Control 19, 68–76 (2015)CrossRefGoogle Scholar
  14. 14.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) European Conference on Computer Vision, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Electronic EngineeringGuangxi Normal UniversityGuilinChina

Personalised recommendations