Skip to main content

Efficient Region-based Classification for Whole Slide Images

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics - Theory and Applications (VISIGRAPP 2014)


For the past decade, new hardware able to generate very high spatial resolution digital images called Whole Slide Images (WSIs) have been challenging traditional microscopy. But the potential for automation is hindered by the large size of the files, possibly tens of billions of pixels. We propose a fast segmentation method coupled with an intuitive multiclass supervised classification that captures expert knowledge presented as morphological annotations to establish a cartography of a WSI and highlight biological regions of interest. While our primary focus has been the development of a proof of concept for the analysis of breast cancer WSIs acquired after chromogenic immunohistochemistry, this method could also be applied to more general texture-based problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions


  1. Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. In: IEEE Reviews in Biomedical Engineering (2009)

    Google Scholar 

  2. Tavassoli, F.A., Devilee, P.: Pathology and Genetics of Tumours of the Breast and Female Genital Organs. IARCPress, Lyon (2003)

    Google Scholar 

  3. Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: Whole-slide imaging and beyond. In: Annual Review of Pathology (2013)

    Google Scholar 

  4. Signolle, N., Plancoulaine, B., Herlin, P., Revenu, M.: Texture-based multiscale segmentation: application to stromal compartment characterization on ovarian carcinoma virtual slides. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 173–182. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Huang, C.H., Veillard, A., Roux, L., Loménie, N., Racoceanu, D.: Time-efficient sparse analysis of histopathological whole slide images. Comput. Med. Imaging Graph. (2010)

    Google Scholar 

  6. Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. I. the value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology (1991)

    Google Scholar 

  7. Ruiz, A., Sertel, O., Ujaldon, M., Catalyurek, U., Saltz, J., Gurcan, M.: Pathological image analysis using the gpu: stroma classification for neuroblastoma. In: 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007) (2007)

    Google Scholar 

  8. Sertel, O., Kong, J., Shimada, H., Catalyurek, U.: Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recogn. (2009)

    Google Scholar 

  9. Roullier, V., Lézoray, O., Ta, V.T., Elmoataz, A.: Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Comput. Med. Imaging Graph. (2011)

    Google Scholar 

  10. Homeyer, A., Schenk, A., Arlt, J., Dahmen, U., Dirsch, O., Hahn, H.K.: Practical quantification of necrosis in histological whole-slide images. Comput. Med. Imaging Graph. (2013)

    Google Scholar 

  11. Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  12. Montanari, U.: On the optimal detection of curves in noisy pictures. Commun. ACM (1971)

    Google Scholar 

  13. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. (1996)

    Google Scholar 

  14. Wemmert, C., Krüger, J., Forestier, G., Sternberger, L., Feuerhake, F., Gançarski, P.: Stain unmixing in brightfield multiplexed immunohistochemistry. In: IEEE International Conference on Image Processing (2013)

    Google Scholar 

  15. Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. (2006)

    Google Scholar 

  16. Haindl, M., Mikes̆, S.: Texture segmentation benchmark. In: Proceedings of the 19th International Conference on Pattern Recognition (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Grégory Apou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Apou, G., Naegel, B., Forestier, G., Feuerhake, F., Wemmert, C. (2015). Efficient Region-based Classification for Whole Slide Images. In: Battiato, S., Coquillart, S., Pettré, J., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics - Theory and Applications. VISIGRAPP 2014. Communications in Computer and Information Science, vol 550. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25116-5

  • Online ISBN: 978-3-319-25117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics