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Content-Based Retrieval of Medical Images by Combining Global Features

  • Mark O Güld
  • Christian Thies
  • Benedikt Fischer
  • Thomas M. Lehmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)

Abstract

A combination of several classifiers using global features for the content description of medical images is proposed. Beside well known texture histogram features, downscaled representations of the original images are used, which preserve spatial information and utilize distance measures which are robust with regard to common variations in radiation dose, translation, and local deformation. These features were evaluated for the annotation task and the retrieval task in ImageCLEF 2005 without using additional textual information or query refinement mechanisms. For the annotation task, a categorization rate of 86.7% was obtained, which ranks second among all submissions. When applied in the retrieval task, the image content descriptors yielded a mean average precision (MAP) of 0.0751, which is rank 14 of 28 submitted runs. As the image deformation model is not fit for interactive retrieval tasks, two mechanisms are evaluated with regard to the trade-off between loss of accuracy and speed increase: hierarchical filtering and prototype selection.

Keywords

Global Feature Query Image Mean Average Precision Retrieval Task Annotation Task 
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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References

  1. 1.
    Clough, P., Müller, 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
  2. 2.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  3. 3.
    Güld, M.O., Keysers, D., Deselaers, T., Leisten, M., Schubert, H., Ney, H., Lehmann, T.M.: Comparison of global features for categorization of medical images. In: Proceedings SPIE, vol. 5371, pp. 211–222 (2004)Google Scholar
  4. 4.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics 8(6), 460–472 (1978)CrossRefGoogle Scholar
  5. 5.
    Puzicha, J., Rubner, Y., Tomasi, C., Buhmann, J.: Empirical evaluation of dissimilarity measures for color and texture. In: Proceedings International Conference on Computer Vision, vol. 2, pp. 1165–1173 (1999)Google Scholar
  6. 6.
    Keysers, D., Gollan, C., Ney, H.: Classification of medical images using non-linear distortion models. In: Bildverarbeitung für die Medizin, pp. 366–370. Springer, Berlin (2004)Google Scholar
  7. 7.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–36 (2000)CrossRefGoogle Scholar
  8. 8.
    Pekalska, E., Duin, R.P.W., Paclik, P.: Prototype selection for dissimilarity-based classification. Pattern Recognition (to appear)Google Scholar
  9. 9.
    Lehmann, T.M., Güld, M.O., Thies, C., Fischer, B., Spitzer, K., Keysers, D., Ney, H., Kohnen, M., Schubert, H., Wein, B.B.: Content-based image retrieval in medical applications. Methods of Information in Medicine 43(4), 354–361 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark O Güld
    • 1
  • Christian Thies
    • 1
  • Benedikt Fischer
    • 1
  • Thomas M. Lehmann
    • 1
  1. 1.Department of Medical InformaticsRWTH AachenAachenGermany

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