Advertisement

Virus Texture Analysis Using Local Binary Patterns and Radial Density Profiles

  • Gustaf Kylberg
  • Mats Uppström
  • Ida-Maria Sintorn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

Keywords

virus morphology texture analysis local binary patterns radial density profiles 

References

  1. 1.
    Goldsmith, C.S., Miller, S.E.: Modern uses of electron microscopy for detection of viruses. Clin. Microbiol. Rev. 22(4), 552–563 (2009)CrossRefGoogle Scholar
  2. 2.
    Biel, S.S., Madeley, D.: Diagnostic virology – the need for electron microscopy: a discussion paper. J. Clin. Virol. 22(1), 1–9 (2001)CrossRefGoogle Scholar
  3. 3.
    Matuszewski, B.J., Shark, L.K.: Hierarchical iterative bayesian approach to automatic recognition of biological viruses in electron microscope images. In: Proc. of 2001 International Conference on Image Processing (ICIP), vol. 2, pp. 347–350 (2001)Google Scholar
  4. 4.
    Ong, H.C.L.: Virus recognition in electron microscope images using higher order spectral features. PhD thesis, Queensland University of Technology (2006)Google Scholar
  5. 5.
    Harwood, D., Ojala, T., Pietikäinen, M., Kelman, S., Davis, L.: Texture classification by center-symmetric auto-correlation, using kullback discrimination of distributions. Pattern. Recogn. Lett. 16(1), 1–10 (1995)CrossRefGoogle Scholar
  6. 6.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern. Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  7. 7.
    Hervé, N., Servais, A., Thervet, E., Olivo-Marin, J.C., Meas-Yedid, V.: Statistical color texture descriptors for histological images analysis. In: Proc. of IEEE International Symposium on Biomedical Imaging (ISBI), pp. 724–727 (2011)Google Scholar
  8. 8.
    Zhang, B.: Classification of subcellular phenotype images by decision templates for classifier ensemble. In: Pham, T., Zhou, X. (eds.) Proc. of 2009 International Conference on Computational Models for Life Sciences (CMLS), pp. 13–22 (2010)Google Scholar
  9. 9.
    Mäenpää, T.: The local binary pattern approach to texture analysis - extensions and applications. PhD thesis, University of Oulu (2003)Google Scholar
  10. 10.
    Sintorn, I.M., Homman-Loudiyi, M., Söderberg-Nauclér, C., Borgefors, G.: A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images. Comput. Meth. Prog. Bio. 76, 95–102 (2004)CrossRefGoogle Scholar
  11. 11.
    Bhella, D., Rixon, F.J., Dargan, D.J.: Cryomicroscopy of human cytomegalovirus virions reveals more densely packed genomic DNA than in herpes simplex virus type 1. J. Mol. Biol. 295, 155–161 (2000)CrossRefGoogle Scholar
  12. 12.
    Trus, B.S., Gibson, W., Cheng, N., Steven, A.C.: Capsid structure of Simian cytomegalovirus from cryoelectron microscopy: Evidence for tegument attachment sites. J. Virol. 73(3), 2181–2192 (1999)Google Scholar
  13. 13.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kylberg, G., Uppström, M., Hedlund, K.O., Borgefors, G., Sintorn, I.M.: Segmentation of virus particle candidates in transmission electron microscopy images (manuscript, 2011)Google Scholar
  15. 15.
    Mäenpää, T., Ojala, T., Pietikäinen, M., Soriano, M.: Robust texture classification by subsets of local binary patterns. In: Proc. of International Conference on Pattern Recognition (ICPR), pp. 3947–3950 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gustaf Kylberg
    • 1
  • Mats Uppström
    • 2
  • Ida-Maria Sintorn
    • 1
  1. 1.Centre for Image AnalysisUppsalaSweden
  2. 2.Vironova ABStockholmSweden

Personalised recommendations