Histopathology Image Classification Using Bag of Features and Kernel Functions

  • Juan C. Caicedo
  • Angel Cruz
  • Fabio A. Gonzalez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)


Image representation is an important issue for medical image analysis, classification and retrieval. Recently, the bag of features approach has been proposed to classify natural scenes, using an analogy in which visual features are to images as words are to text documents. This process involves feature detection and description, construction of a visual vocabulary and image representation building through visual-word occurrence analysis. This paper presents an evaluation of different representations obtained from the bag of features approach to classify histopathology images. The obtained image descriptors are processed using appropriate kernel functions for Support Vector Machines classifiers. This evaluation includes extensive experimentation of different strategies, and analyses the impact of each configuration in the classification result.


Kernel Function Image Retrieval Image Representation Feature Representation Natural Scene 
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.
    Bosch, A., Muñoz, X., Martí, R.: Which is the best way to organize/classify images by content? Image and Vision Computing 25, 778–791 (2007)CrossRefGoogle Scholar
  2. 2.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision (2004)Google Scholar
  3. 3.
    Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos 2, 1470–1477 (2003)Google Scholar
  4. 4.
    Tommasi, T., Orabona, F., Caputo, B.: CLEF2007 Image annotation task: An SVM-based cue integration approach. In: Working Notes of the 2007 CLEF Workshop, Budapest, Hungary (2007)Google Scholar
  5. 5.
    Iakovidis, D.K., Pelekis, N., Kotsifakos, E.E., Kopanakis, I., Karanikas, H., Theodoridis, Y.: A pattern similarity scheme for medical image retrieval. IEEE Transactions on Information Technology in Biomedicine (2008)Google Scholar
  6. 6.
    Long, L.R., Antani, S.K., Thoma, G.R.: Image informatics at a national research center. Computerized Medical Imaging and Graphics 29, 171–193 (2005)CrossRefPubMedGoogle Scholar
  7. 7.
    Guld, M.O., Keysers, D., Deselaers, T., Leisten, M., Schubert, H., Ney, H., Lehmann, T.M.: Comparison of global features for categorization of medical images. Medical Imaging 5371, 211–222 (2004)Google Scholar
  8. 8.
    Deselaers, T., Keysers, D., Ney, H.: FIRE - Flexible Image Retrieval Engine: imageCLEF 2004 evaluation. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 688–698. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Datar, M., Padfield, D., Cline, H.: Color and texture based segmentation of molecular pathology images using hsoms. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008, pp. 292–295 (2008)Google Scholar
  10. 10.
    Comaniciu, D., Meer, P., Foran, D.: Shape-based image indexing and retrieval for diagnostic pathology. In: Proceedings on Fourteenth International Conference on Pattern Recognition, vol. 1, pp. 902–904 (1998)Google Scholar
  11. 11.
    Caicedo, J.C., Gonzalez, F.A., Romero, E.: A semantic content-based retrieval method for histopathology images. In: Li, H., Liu, T., Ma, W.-Y., Sakai, T., Wong, K.-F., Zhou, G. (eds.) AIRS 2008. LNCS, vol. 4993, pp. 51–60. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Zheng, L., Wetzel, A.W., Gilbertson, J., Becich, M.J.: Design and analysis of a content-based pathology image retrieval system. IEEE Transactions on Information Technology in Biomedicine 7(4), 249–255 (2003)CrossRefPubMedGoogle Scholar
  13. 13.
    Lam, R.W.K., Ip, H.H.S., Cheung, K.K.T., Tang, L.H.Y., Hanka, R.: A multi-window approach to classify histological features. In: Proceedings on 15th International Conference on Pattern Recognition, vol. 2, pp. 259–262 (2000)Google Scholar
  14. 14.
    Tang, H.L., Hanka, R., Ip, H.H.S.: Histological image retrieval based on semantic content analysis. IEEE Transactions on Information Technology in Biomedicine 7(1), 26–36 (2003)CrossRefPubMedGoogle Scholar
  15. 15.
    Fletcher, C.D.M.: Diagnostic Histopathology of tumors. Elsevier Science, Amsterdam (2003)Google Scholar
  16. 16.
    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification, pp. 490–503 (2006)Google Scholar
  17. 17.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  18. 18.
    Li, F.F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Washington, DC, USA, vol. 2, pp. 524–531. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  19. 19.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Juan C. Caicedo
    • 1
  • Angel Cruz
    • 1
  • Fabio A. Gonzalez
    • 1
  1. 1.Bioingenium Research GroupNational University of ColombiaColombia

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