Supervised Machine Learning Based Medical Image Annotation and Retrieval in ImageCLEFmed 2005

  • Md. Mahmudur Rahman
  • Bipin C. Desai
  • Prabir Bhattacharya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)


This paper presents the methods and experimental results for the automatic medical image annotation and retrieval task of ImageCLEFmed 2005. A supervised machine learning approach to associate low-level image features with their high level visual and/or semantic categories is investigated. For automatic image annotation, the input images are presented as a combined feature vector of texture, edge and shape features. A multi-class classifier based on pairwise coupling of several binary support vector machine is trained on these inputs to predict the categories of test images. For visual only retrieval, a combined feature vector of color, texture and edge features is utilized in low dimensional PCA sub-space. Based on the online category prediction of query and database images by the classifier, pre-computed category specific first and second order statistical parameters are utilized in a Bhattacharyya distance measure. Experimental results of both image annotation and retrieval are reported in this paper.


Feature Vector Image Retrieval Image Annotation Supervise Machine Learn Support Vector Machine Training 
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.
    Tagare, H.D., Jafe, C., Duncan, J.: Medical image databases: A content-based retrieval approach. Journal of the American Medical Informatics Association 4 (3), 184–198 (1997)Google Scholar
  2. 2.
    Muller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval applications -clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2004)CrossRefGoogle Scholar
  3. 3.
    Clough, P., Muller, H., Deselaers, T., Grubinger, M., Lehmann, T.M., Jensen, J., Hersh, W.: The CLEF 2005 Cross–Language Image Retrieval Track. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 535–557. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Haralick, R.M., Shanmugam, D.I.: Textural features for image classification. IEEE Trans System, Man, Cybernetics SMC-3, 610–621 (1973)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. 8, 679–698 (1986)CrossRefGoogle Scholar
  6. 6.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Information Theory 8 (1962)Google Scholar
  7. 7.
    Chapelle, O., Haffner, P., Vapnik, V.: SVMs for histogram-based image classification. IEEE Transaction on Neural Networks 10(5), 1055–1064 (1999)CrossRefGoogle Scholar
  8. 8.
    Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  9. 9.
    Wu, T.F., Lin, C.J., Weng, R.C.: Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research 5, 975–1005 (2004)MathSciNetGoogle Scholar
  10. 10.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at
  11. 11.
    Smeulder, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. on Pattern Anal. and Machine Intell. 22, 1349–1380 (2000)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Bhandrasekaran, B.: Dimensionality and sample size considerations in pattern recognition practice. Handbook of Statistics 2, 835–855 (1987)CrossRefGoogle Scholar
  13. 13.
    Aksoy, S., Haralick, R.M.: Probabilistic vs. geometric similarity measures for image retrieval. Proceedings. IEEE Conference on Computer Vision and Pattern Recognition 2, 357–362 (2000)Google Scholar
  14. 14.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Professional, Inc., San Diego (1990)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Md. Mahmudur Rahman
    • 1
  • Bipin C. Desai
    • 1
  • Prabir Bhattacharya
    • 2
  1. 1.Dept. of Computer ScienceConcordia UniversityCanada
  2. 2.Institute for Information Systems EngineeringConcordia UniversityCanada

Personalised recommendations