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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics 8(6), 460–472 (1978)
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)
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)
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)
Pekalska, E., Duin, R.P.W., Paclik, P.: Prototype selection for dissimilarity-based classification. Pattern Recognition (to appear)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Güld, M.O., Thies, C., Fischer, B., Lehmann, T.M. (2006). Content-Based Retrieval of Medical Images by Combining Global Features. In: Peters, C., et al. Accessing Multilingual Information Repositories. CLEF 2005. Lecture Notes in Computer Science, vol 4022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11878773_77
Download citation
DOI: https://doi.org/10.1007/11878773_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45697-1
Online ISBN: 978-3-540-45700-8
eBook Packages: Computer ScienceComputer Science (R0)