Colour Texture Segmentation of Tear Film Lipid Layer Images

  • B. Remeseiro-López
  • L. Ramos
  • N. Barreira Rodríguez
  • A. Mosquera
  • E. Yebra-Pimentel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8112)


Dry eye is a symptomatic disease which can be diagnosed by several clinical tests. One of them is the evaluation of the interference lipid pattern and its classification into one of the Guillon categories. Previous researches have automatised this manual test, saving time for experts and providing unbiased results. However, the heterogeneity of the tear film lipid layer makes its classification into a single category per eye impossible. For this reason, this paper presents a first approximation to segment tear film images into the Guillon categories, in order to detect several categories in each patient. The adequacy of the methodology was demonstrated since it achieves reliable results in comparison with the annotations done by optometrists.


tear film lipid layer Guillon categories colour texture analysis image segmentation 


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  1. 1.
    Lemp, M., Baudouin, C., Baum, J., Dogru, M., Foulks, G., Kinoshita, S., Laibson, P., McCulley, J., Murube, J., Pfugfelder, S., Rolando, M., Toda, I.: The definition and classification of dry eye disease: Report of the definition and classification subcommittee of the international dry eye workshop. Ocul. Surf. 5(2), 75–92 (2007)CrossRefGoogle Scholar
  2. 2.
    Guillon, J.: Non-invasive tearscope plus routine for contact lens fitting. Cont. Lens Anterior Eye 21(suppl. 1) (1998)Google Scholar
  3. 3.
    Foulks, G.: The correlation between the tear film lipid layer and dry eye disease. In: Surv. Ophthalmol, vol. 52, pp. 369–374 (2007)Google Scholar
  4. 4.
    Ramos, L., Penas, M., Remeseiro, B., Mosquera, A., Barreira, N., Yebra-Pimentel, E.: Texture and color analysis for the automatic classification of the eye lipid layer. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part II. LNCS, vol. 6692, pp. 66–73. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Remeseiro, B., Ramos, L., Penas, M., Martínez, E., Penedo, M., Mosquera, A.: Colour texture analysis for classifying the tear film lipid layer: a comparative study. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), Noosa, Australia, pp. 268–273 (December 2011)Google Scholar
  6. 6.
    Bolón-Canedo, V., Peteiro-Barral, D., Remeseiro, B., Alonso-Betanzos, A., Guijarro-Berdiñas, B., Mosquera, A., Penedo, M., Sánchez-Maroño, N.: Interferential Tear Film Lipid Layer Classification: an Automatic Dry Eye Test. In: IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Athens, Greece, pp. 359–366 (November 2012)Google Scholar
  7. 7.
    Tearscope plus clinical hand book and tearscope plus instructions. Keeler Ltd. Windsor, Berkshire, Keeler Inc., Broomall (1997)Google Scholar
  8. 8.
    Topcon SL-D4 slit lamp Topcon Medical Systems, Oakland, NJ, USAGoogle Scholar
  9. 9.
    Topcon DV-3 digital video camera Topcon Medical Systems, Oakland, NJ, USAGoogle Scholar
  10. 10.
    Topcon IMAGEnet i-base Topcon Medical Systems, Oakland, NJ, USAGoogle Scholar
  11. 11.
    Calvo, D., Mosquera, A., Penas, M., García-Resúa, C., Remeseiro, B.: Color Texture Analysis for Tear Film Classification: A Preliminary Study. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 388–397. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    McLaren, K.: The development of the CIE 1976 (L*a*b) uniform colour-space and colour-difference formula. Journal of the Society of Dyers and Colourists 92(9), 338–341 (1976)CrossRefGoogle Scholar
  13. 13.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics In Systems, Man and Cybernetics 3, 610–621 (1973)CrossRefGoogle Scholar
  14. 14.
    Hall, M.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)Google Scholar
  15. 15.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • B. Remeseiro-López
    • 1
  • L. Ramos
    • 1
  • N. Barreira Rodríguez
    • 1
  • A. Mosquera
    • 2
  • E. Yebra-Pimentel
    • 3
  1. 1.Dpto. de ComputaciónUniv. da CoruñaSpain
  2. 2.Dpto. de Electrónica y ComputaciónUniv. de Santiago de CompostelaSpain
  3. 3.Facultad de Óptica y OptometríaUniv. de Santiago de CompostelaSpain

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