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A New Retinal Recognition System Using a Logarithmic Spiral Sampling Grid

  • Fabiola M. Villalobos Castaldi
  • Edgardo M. Felipe-Riveron
  • Ernesto Suaste Gómez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)

Abstract

The retinal vascular network has many desirable characteristics as a basis for authentication, including uniqueness, stability, and permanence. In this paper, a new approach for retinal images features extraction and template coding is proposed. The use of the logarithmic spiral sampling grid in scanning and tracking the vascular network is the key to make this new approach simple, flexible and reliable. Experiments show that this approach can achieve the reduction of data dimensionality and of the required time to obtain the biometric code of the vascular network in a retinal image. The performed experiments demonstrated that the proposed verification system has an average accuracy of 95.0 – 98 %.

Keywords

Logarithmic spiral sampling grid spiral scan and sampling biometry retinal images time series representations 

References

  1. 1.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. In: Proceedings of ACM SIGMOD Conference on Management of Data, Santa Barbara, CA, May 21-24, pp. 151–162 (2001)Google Scholar
  2. 2.
    Jung, E., Hong, K.: Automatic Retinal Vasculature Structure Tracing and Vascular Landmark Extraction from Human Eye Image. In: Proceedings of the International Conference on Hybrid Information Technology. IEEE Computer Society (2006)Google Scholar
  3. 3.
    Hill, R.B.: Retina Identification, Portland, OR, USA (1992)Google Scholar
  4. 4.
    Hill, R.B.: Apparatus and method for identifying individuals through their retinal vasculature patterns. U.S. Patent No. 4109237 (1978)Google Scholar
  5. 5.
    Simon, C., Goldstein, I.: A new scientific method of identification. New York State. J. Medicine 35(18), 901–906 (1935)Google Scholar
  6. 6.
    Tower, P.: The fundus oculi in monozygotic twins: Report of six pairs of identical twins. Arch. Ophthalmol. 54, 225–239 (1955)CrossRefGoogle Scholar
  7. 7.
    Marshall, J., Usher, D.: Method for generating a unique and consistent signal pattern for identification of an individual. U.S. Patent No. 6993161 (2006)Google Scholar
  8. 8.
    Derakhshani, R., Ross, A.: A Texture-Based Neural Network Classifier for Biometric Identification using Ocular Surface Vasculature. In: Appeared in Proc. of International Joint Conference on Neural Networks (IJCNN), Orlando, USA (2007)Google Scholar
  9. 9.
    Golden, B.L., Rollin, B.E., Switzer, J.R.V.: Apparatus and method for creating a record using biometric information. U.S. Patent No. 028343 (2004)Google Scholar
  10. 10.
    Ortega, M., Gonzalez, M.F.: Automatic system for personal authentication using the retinal vessel tree as biometric pattern, PhD. Thesis, Department of Computer Science of the Faculty of Informatics of the University of Coruña (2009), downloaded from: http://www.varpa.es/ (revised on June 10, 2012)
  11. 11.
  12. 12.
    Bevilacqua, V., Cambó, S., Cariello, L., Mastronardi, G.: Retinal Fundus Hybrid Analysis Based on Soft Computing Algorithms. Communications To Simai Congress 2 (2007) ISSN 1827-9015Google Scholar
  13. 13.
    Usher, D., Tosa, Y., Friedman, M.: Ocular Biometrics: Simultaneous Capture and Analysis of the Retina and Iris. Advances in Biometrics Sensors, Algorithms and Systems, 133–155 (2007)Google Scholar
  14. 14.
    Usher, D.B.: Image analysis for the screening of diabetic retinopathy, PhD thesis, University of London (2003)Google Scholar
  15. 15.
    Fukuta, K., Nakagawa, T., Yoshinori, H., Hatanaka, Y., Hara, T., Fujita, H.: Personal identification based on blood vessels of retinal fundus images (Proceedings Paper). In: Medical Imaging, Image Processing, Proceedings, vol. 6914 (2008)Google Scholar
  16. 16.
    Lee, S.S., Rajeswari, M., Ramachandram, D., Shaharuddin, B.: Screening of Diabetic Retinopathy - Automatic Segmentation of Optic Disc in Colour Fundus Images. In: Proc. 2nd International Conference on Distributed Frameworks for Multimedia Applications, pp. 1–7 (2006)Google Scholar
  17. 17.
    MacGillivray, T.J., Patton, N., Doubal, F.N., Graham, C., Wardlaw, J.M.: Fractal analysis of the retinal vascular network in fundus images. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, France (2007)Google Scholar
  18. 18.
    Barry, R.: Fractal analysis of the vascular tree in the human retina, Masters. Annu. Rev. Biomed. Eng. 6, 427–452 (2004), doi:10.1146/annurev.bioeng.6.040803.140100CrossRefGoogle Scholar
  19. 19.
    Taylor, R.P.: Chaos, Fractals, Nature: a New look at Jackson Pollock, Fractals Research, Eugene OR (200&)Google Scholar
  20. 20.
  21. 21.
    http://www.2dcurves.com/spiral/spirallo.html (revised on September 20, 2012)
  22. 22.
    Zana, F., Klein, J.C.: Robust Segmentation of Vessels from Retinal Angiography. In: International Conference on Digital Signal Processing, Santorini, Greece, pp. 1087–1091 (1977)Google Scholar
  23. 23.
    Zhoue, L., Rzeszotarski, M., Singerman, L., Cokreff, J.: The detection and quantification of retinopathy using digital angiograms. IEEE Transaction on Medical Imaging 13-4, 619-626 (1994)Google Scholar
  24. 24.
    Matsopoulos, G.K., Mouravliansky, N.A., Delibasis, K.K., Nikita, K.S.: Automatic retinal image registration Scheme using global optimization techniques. IEEE Trans. Information Technology in Biomedicine 3 (1999)Google Scholar
  25. 25.
    Wang, L., Bhalerao, A.: Model based segmentation for retinal fundus images. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 422–429. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  26. 26.
    Farzin, H., Abrishami-Moghaddam, H., Moin, M.: A Novel Retinal Identification System. EURASIP Journal on Advances in Signal Processing, Article ID 280635, 10 pages (2008), doi:10.1155/2008/280635Google Scholar
  27. 27.
    Arakala, A., Culpepper, J.S., Jeffers, J., Turpin, A., Boztaş, S., Horadam, K.J., McKendrick, A.M.: Entropy of the Retina Template. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1250–1259. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  28. 28.
    Fuhrmann, T., Hammerle-Uhl, J., Uhl, A.: Usefulness of Retina Codes in Biometrics. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 624–632. Springer, Heidelberg (2009), doi:10.1007/978-3-540-92957-4_54.CrossRefGoogle Scholar
  29. 29.
    Che Azemin, M.Z., Kumar, D.K., Wu, H.R.: Shape Signature for Retinal Biometrics. In: 2009 Digital Image Computing: Techniques and Applications, DICTA 2009, pp. 382–386 (2009)Google Scholar
  30. 30.
  31. 31.
    Chen, J., Moon, Y.-S., Wong, M.-F., Su, G.: Palmprint authentication using a symbolic representation of images. Image and Vision Computing 28, 343–351 (2010)CrossRefGoogle Scholar
  32. 32.
  33. 33.
    MacGillivray, T.J., Patton, N., Doubal, F.N., Graham, C., Wardlaw, J.M.: Fractal analysis of the retinal vascular network in fundus images. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, France (2007)Google Scholar
  34. 34.
    Barry, R.: Fractal analysis of the vascular tree in the human retina, Masters. Annu. Rev. Biomed. Eng. 6, 427–452 (2004), doi:10.1146/annurev.bioeng.6.040803.140100.CrossRefGoogle Scholar
  35. 35.
    Taylor, R.P.: Chaos, Fractals, Nature: a New look at Jackson Pollock, Fractals Research, Eugene OR (200&)Google Scholar
  36. 36.
    Villalobos, F.M., Felipe, E.F.: A Fast Efficient and Automated Method to Extract Vessels from Fundus Images. Journal of Visualization, J. Vis. 13, 263–270 (2010) ISSN: 1343-8875, doi:10.1007/s12650-010-0037-yGoogle Scholar
  37. 37.
  38. 38.
  39. 39.
    Yu, H., Barriga, S., Agurto, C., Echegaray, S., Pattichis, M., Zamora, G., Bauman, W., Soliz, P.: Fast Localization of Optic Disc and Fovea in Retinal Images for Eye Disease Screening (2008), http://visionquest-bio.com/index.html
  40. 40.
    Youssif, A.A.A., Ghalwash, A.Z., Ghoneim, A.A.S.A.: Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels’ Direction Matched. IEEE Transactions on Medical Imaging 27(1), 11–19 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabiola M. Villalobos Castaldi
    • 1
  • Edgardo M. Felipe-Riveron
    • 2
  • Ernesto Suaste Gómez
    • 1
  1. 1.Researches and Advanced Studies Center of the National Polytechnic InstituteMexico D.F.Mexico
  2. 2.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico D.F.Mexico

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