Histology Image Indexing Using a Non-negative Semantic Embedding

  • Jorge A. Vanegas
  • Juan C. Caicedo
  • Fabio A. González
  • Eduardo Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7075)

Abstract

Large on-line collections of biomedical images are becoming more common and may be a potential source of knowledge. An important unsolved issue that is actively investigated is the efficient and effective access to these repositories. A good access strategy demands an appropriate indexing of the collection. This paper presents a new method for indexing histology images using multimodal information, taking advantage of two kinds of data: visual data extracted directly from images and available text data from annotations performed by experts. The new strategy called Non-negative Semantic Embedding defines a mapping between visual an text data assuming that the latent space spanned by text annotations is good enough representation of the images semantic. Evaluation of the proposed method is carried out by comparing it with other strategies, showing a remarkable image search improvement since the proposed approach effectively exploits the image semantic relationships.

Keywords

Latent Factor Visual Feature Text Data Mean Average Precision Semantic Space 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Roa-Peña, L., Gómez, F., Romero, E.: An experimental study of pathologist’s navigation patterns in virtual microscopy. Diagnostic Pathology 5(1), 71 (2010)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.
    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)CrossRefGoogle Scholar
  4. 4.
    Caicedo, J.C., González, F.A., Triana, E., Romero, E.: Design of a Medical Image Database with Content-Based Retrieval Capabilities. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 919–931. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Yu, F., Ip, H.: Semantic content analysis and annotation of histological images. Computers in Biology and Medicine 38(6), 635–649 (2008)CrossRefGoogle Scholar
  6. 6.
    Caicedo, J.C., Cruz, 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
  7. 7.
    Müller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: Experimental Evaluation in Visual Information Retrieval. Springer, Heidelberg (2010)CrossRefMATHGoogle Scholar
  8. 8.
    González, F.A., Caicedo, J.C., Nasraoui, O., Ben-Abdallah, J.: NMF-based multimodal image indexing for querying by visual example. In: ACM International Conference On Image And Video Retrieval, pp. 366–373. ACM Press, New York (2010)Google Scholar
  9. 9.
    Liu, W., Zheng, N., Lu, X.: Non-negative matrix factorization for visual coding. In: 2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003 Proceedings, vol. 3, pp. III–293–6. IEEE (2003)Google Scholar
  10. 10.
    Lee, D.D., Seung, H.S.: New Algorithms for Non-Negative Matrix Factorization in Applications to Blind Source Separation. In: 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, vol. 13(1), pp. V–621–V–624 (2001)Google Scholar
  11. 11.
    Díaz, G., Romero, E.: Histopathological Image Classification using Stain Component Features on a pLSA Model. In: Bloch, I., Cesar Jr., R.M. (eds.) CIARP 2010. LNCS, vol. 6419, pp. 55–62. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Cruz-Roa, A., Caicedo, J.C., González, F.A.: Visual Pattern Analysis in Histopathology Images Using Bag of Features. In: Bayro-Corrochano, E., Eklundh, J.-O. (eds.) CIARP 2009. LNCS, vol. 5856, pp. 521–528. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)CrossRefGoogle Scholar
  14. 14.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jorge A. Vanegas
    • 1
  • Juan C. Caicedo
    • 1
  • Fabio A. González
    • 1
  • Eduardo Romero
    • 1
  1. 1.Bioingenium Research GroupNational University of ColombiaColombia

Personalised recommendations