Histopathological Image Classification Using Stain Component Features on a pLSA Model

  • Gloria Díaz
  • Eduardo Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


Semantic annotation of microscopical field of views is one of the key problems in computer assistance of histopathological images. In this paper a new method for extracting patch descriptors is proposed and evaluated using a probabilistic latent semantic analysis (pLSA) classification model. The proposed approach is based on the analysis of the different dyes used to stain the histological sample. This analysis allows to find local regions that correspond to cells in the image, which are then described by the SIFT descriptors of the stain components. The proposed approach outperforms the conventional sampling and description strategies, proposed in the literature.


Semantic annotation Histopathological Images Color decomposition pLSA SIFT descriptors 


  1. 1.
    Romero, E., Gomez, F., Iregui, M.: Virtual Microscopy in Medical Images: a Survey. In: Modern Research and Educational Topics in Microscopy. Formatex (2007)Google Scholar
  2. 2.
    Gurcan, M., Boucheron, L., Can, A., Madabhushi, A., Rajpoot, N., Yener, B.: Histopathological image analysis: A review. IEEE Reviews in Biomedical Engineering 2, 147–171 (2009)CrossRefGoogle Scholar
  3. 3.
    André, B., Vercauteren, T., Perchant, A., Buchner, A.M., Wallace, M.B., Ayache, N.: Endomicroscopic image retrieval and classification using invariant visual features. In: Proceedings of International Symposium on Biomedical Imaging (2009)Google Scholar
  4. 4.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42, 177–196 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Caicedo, J.C., Cruz-Roa, A., Gonzalez, F.A.: Histopathology image classification using bag of features and kernel functions. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) AIME 2009. LNCS, vol. 5651, pp. 126–135. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: International Conference on Computer Vision (2003)Google Scholar
  7. 7.
    Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Transactions on Computer Graphics and Applications 21, 34–41 (2001)CrossRefGoogle Scholar
  8. 8.
    Lee, D.D., Seung, H.S.: Algorithms for nonnegative matrix factorization. Advances in Neural Information Processing Systems 13, 556–562 (2001)Google Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gloria Díaz
    • 1
  • Eduardo Romero
    • 1
  1. 1.Bioingenium Research GroupNational University of ColombiaBogotáColombia

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