Retinal Image Classification for the Screening of Age-Related Macular Degeneration

  • Mohd Hanafi Ahmad Hijazi
  • Frans Coenen
  • Yalin Zheng
Conference paper


Age-related Macular Degeneration (AMD) is the most common cause of blindness in old-age. Early identification of AMD can allow for mitigation (but not cure). One of the fist symptoms of AMD is the presence of fatty deposits, called drusen, on the retina. The presence of drusen may be identified through inspection of retina images. Given the aging global population, the prevalence of AMD is increasing. Many health authorities therefore run screening programmes. The automation, or at least partial automation, of retina image screening is therefore seen as beneficial. This paper describes a Case Based Reasoning (CBR) approach to retina image classification to provide support for AMD screening programmes. In the proposed approach images are represented in the form of spatial-histograms that store both colour and spatial image information. Each retina image is represented using a series of histograms each encapsulated as a time series curve. The Case Base (CB) is populated with a labelled set of such curves. New cases are classified by finding the most similar case (curve) in the CB. Similarity checking is achieved using the Dynamic Time warping (DTW).


Feature Selection Image Retrieval Retinal Image Dynamic Time Warping Case Base Reasoning 
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.


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  1. 1.
    D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In AAAI Workshop on Knowledge Discovery in Databases, pages 229–248, 1994.Google Scholar
  2. 2.
    I. I. Bichindaritz and C. C. Marling. Case-based reasoning in the health science: What’s next? Artificial Intelligence in Medicine, 36(2):127–135, 2006.CrossRefGoogle Scholar
  3. 3.
    S. T. Birchfield and S. Rangarajan. Spatial histograms for region-based tracking. ETRI Journal, 29(5):697–699, 2007.CrossRefGoogle Scholar
  4. 4.
    L. Brandon and A. Hoover. Drusen detection in a retinal image using multi-level analysis. In Proceedings of Medical Image Computing and Computer-Assisted Intervention, pages 618–625. Springer-Verlag, 2003.Google Scholar
  5. 5.
    R. Brunelli and O. Mich. Histograms analysis for image retrieval. Pattern Recognition Letters, 34:1625–1637, 2001.MATHGoogle Scholar
  6. 6.
    E. Cantu-Paz. Feature subset selection, class separability, and genetic algorithms. In Proceedings of Genetic and Evolutionary Computation Conference, pages 959–970, 2004.Google Scholar
  7. 7.
    E. Cantu-Paz, S. Newsam, and C. Kamath. Feature selection in scientific applications. In Proceedings of 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 788–793, 2004.Google Scholar
  8. 8.
    P. T. V. M. de Jong. Age-related macular degeneration. The New England Journal of Medicine, 355(14):1474–1485, 2006.CrossRefGoogle Scholar
  9. 9.
    U. M. Fayyad, P. Smyth, N. Weir, and S. Djorgovski. Automated analysis and exploration of image databases: Results, progress, and challenges. Journal of Intelligent Information Systems, 4:7–25, 1995.CrossRefGoogle Scholar
  10. 10.
    R. W. Floyd and L. Steinberg. An adaptive algorithm for spatial greyscale. Society for Information Display, 17(2):75–77, 1976.Google Scholar
  11. 11.
    M. Foracchia, E. Grisan, and A. Ruggeri. Luminosity and contrast normalization in retinal images. Medical Image Analysis, 9:179–190, 2005.CrossRefGoogle Scholar
  12. 12.
    G. Forman. An extensive empirical study of feature selection metrics for text classification. Journal of Medical Learning Research, 3:1289–1305, 2003.MATHCrossRefGoogle Scholar
  13. 13.
    D. E. Freund, N. Bressler, and P. Burlina. Automated detection of drusen in the macula. In Proceedings of the Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro, pages 61–64, 2009.Google Scholar
  14. 14.
    R. C. Gonzalez and R. E. Woods. Digital image processing. Pearson Prentice Hall, 2008. 337Google Scholar
  15. 15.
    M. H. A. Hijazi, F. Coenen, and Y. Zheng. A histogram based approach for the screening of age-related macular degeneration. In Medical Image Understanding and Analysis 2009, pages 154–158. BMVA, 2009.Google Scholar
  16. 16.
    M. H. A. Hijazi, F. Coenen, and Y. Zheng. Retinal image classification using a histogram based approach. In Proc. International Joint Conference on Neural Networks, pages 3501–3507. IEEE, 2010.Google Scholar
  17. 17.
    A. Holt, I. Bichindaritz, R. Schmidt, and P. Perner. Medical applications in case-based reasoning. The Knowledge Enginering Review, 20:289–292, 2005.CrossRefGoogle Scholar
  18. 18.
    W. Hsu, S. T Chua, and H. H. Pung. An integrated color-spatial approach to content-based image retrieval. In Proceedings of the Third International Conference on Multimedia, pages 305–313, 1995.Google Scholar
  19. 19.
    W. Hsu, M. L. Lee, and J. Zhang. Image mining: Trends and developments. Intelligent Information Systems, 19(1):7–23, 2002.CrossRefGoogle Scholar
  20. 20.
    R. D. Jager, W. F. Mieler, and J. W. Mieler. Age-related macular degeneration. The New England Journal of Medicine, 358(24):2606–2617, 2008.CrossRefGoogle Scholar
  21. 21.
    J. Kolodner. Case-based reasoning. Morgan Kaufmann, 1993.Google Scholar
  22. 22.
    C. Köse, U. Şevik, and O. Gençalioğlu. Automatic segmentation of age-related macular degeneration in retinal fundus images. Computers in Biology and Medicine, 38:611–619, 2008.CrossRefGoogle Scholar
  23. 23.
    C. Köse, U. Ş evik, and O. Gençalioğlu. A statistical segmentation method for measuring agerelated macular degeneration in retinal fundus images. Journal of Medical Systems, 34(1):1–13, 2008.CrossRefGoogle Scholar
  24. 24.
    D. B. Leake. Case-based reasoning: Experiences, lessons and future directions. AAAI Press/MIT Press, 1996.Google Scholar
  25. 25.
    C. S. Myers and L. R. Rabiner. A comparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal, 60(7):1389–1409, 1981.Google Scholar
  26. 26.
    B. C. Ooi, K-L. Tan, T. S. Chua, and W. Hsu. Fast image retrieval using color-spatial information. The International Journal of Very Large Data Bases, 7(7):115–128, 1998.CrossRefGoogle Scholar
  27. 27.
    A. Osareh. Automated identification of diabetic retinal exudates and the optic disc. PhD thesis, University of Bristol, UK, 2004.Google Scholar
  28. 28.
    N. Patton, T. M. Aslam, and T. MacGillivray. Retinal image analysis: Concepts, applications and potential. Progress in Retinal and Eye Research, 25:99–127, 2006.CrossRefGoogle Scholar
  29. 29.
    K. Rapantzikos, M. Zervakis, and K. Balas. Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration. Medical Image Analysis, 7:95–108, 2003.CrossRefGoogle Scholar
  30. 30.
    Zakaria Ben Sbeh, Laurent D. Cohen, Gerard Mimoun, and Gabriel Coscas. A new approach of geodesic reconstruction for drusen segmentation in eye fundus images. IEEE Transactions on Medical Imaging, 20(12):1321–1333, 2001.CrossRefGoogle Scholar
  31. 31.
    J. V. B. Soares, J. J. G. Leandro, R. M. Cesar Jr., H. F. Jelinek, and M. J. Cree. Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, 25(9):1214–1222, 2006.CrossRefGoogle Scholar
  32. 32.
    M. J. Swain and D. H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–31, 1991.CrossRefGoogle Scholar
  33. 33.
    H-C. Wu and C-C. Chang. An image retrieval method based on color-complexity and spatialhistogram features. Fundamenta Informaticae, 76:481–493, 2007.MATHGoogle Scholar
  34. 34.
    X. Wu. Graphic Gems II, chapter Efficient statistical computations for optimal color quantization, pages 126–133. Elsevier Science and Technology, 1991.Google Scholar
  35. 35.
    H. Zhang, W. Gao, X. Chen, and D. Zhao. Object detection using spatial histograms features. Image and Vision Computing, 24:327–341, 2006.CrossRefGoogle Scholar
  36. 36.
    K. Zuiderveld. Contrast limited adaptive histogram equalization, pages 474–485. Academic Press Graphics Gems Series. Academic Press Professional, Inc., 1994.Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of LiverpoolLiverpoolUK
  2. 2.Ophthalmology Research Unit, School of Clinical SciencesThe University of LiverpoolLiverpoolUK

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