An Improved Correlation Method Based on Rotation Invariant Feature for Automatic Particle Selection

  • Yu Chen
  • Fei Ren
  • Xiaohua Wan
  • Xuan Wang
  • Fa Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8492)

Abstract

Particle selection from cryo-electron microscopy (cryo-EM) images is very important for high-resolution reconstruction of macromolecular structure. However, the accuracy of existing selection methods are normally restricted to noise and low contrast of cryo-EM images. In this paper, we presented an improved correlation method based on rotation invariant features for automatic, fast particle selection. We first selected a preliminary particle set applying rotation invariant features, then filtered the preliminary particle set using correlation to reduce the interference of high noise background and improve the precision of correlation method. We used Divide and Conquer technique and cascade strategy to improve the recognition ability of features and reduce processing time. Experimental results on the benchmark of cryo-EM images show that our method can improve the accuracy of particle selection significantly.

Keywords

Particle selection Rotation invariant feature Correlation Divide and Conquer Cascade strategy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Joachim, F.: Three-dimensional electron microscopy of macromolecular assemblies. Academic Press (1996)Google Scholar
  2. 2.
    Henderson, R.: The potential and limitations of neutrons, electrons and x-rays for atomic resolution microscopy of unstained biological molecules. Quarterly Reviews of Biophysics 28(02), 171–193 (1995)CrossRefGoogle Scholar
  3. 3.
    Sali, A., Glaeser, R., Earnest, T., Baumeister, W.: From words to literature in structural proteomics. Nature 422(6928), 216–225 (2003)CrossRefGoogle Scholar
  4. 4.
    Zhu, Y., Carragher, B., Glaeser, R.M., Fellmann, D., Bajaj, C., Bern, M., Mouche, F., de Haas, F., Hall, R.J., Kriegman, D.J., et al.: Automatic particle selection: results of a comparative study. Journal of Structural Biology 145(1), 3–14 (2004)CrossRefGoogle Scholar
  5. 5.
    Hall, R.J., Patwardhan, A.: A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs. Journal of Structural Biology 145(1), 19–28 (2004)CrossRefGoogle Scholar
  6. 6.
    Mallick, S.P., Zhu, Y., Kriegman, D.: Detecting particles in cryo-em micrographs using learned features. Journal of Structural Biology 145(1), 52–62 (2004)CrossRefGoogle Scholar
  7. 7.
    Zhu, Y., Carragher, B., Mouche, F., Potter, C.S.: Automatic particle detection through efficient hough transforms. IEEE Transactions on Medical Imaging 22(9), 1053–1062 (2003)CrossRefGoogle Scholar
  8. 8.
    Yu, Z., Bajaj, C.: Detecting circular and rectangular particles based on geometric feature detection in electron micrographs. Journal of Structural Biology 145(1), 168–180 (2004)CrossRefGoogle Scholar
  9. 9.
    Sorzano, C., Recarte, E., Alcorlo, M., Bilbao-Castro, J., San-Martín, C., Marabini, R., Carazo, J.: Automatic particle selection from electron micrographs using machine learning techniques. Journal of Structural Biology 167(3), 252–260 (2009)CrossRefGoogle Scholar
  10. 10.
    Abrishami, V., Zaldívar-Peraza, A., de la Rosa-Trevín, J., Vargas, J., Otón, J., Marabini, R., Shkolnisky, Y., Carazo, J., Sorzano, C.: A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs. Bioinformatics 29(19), 2460–2468 (2013)CrossRefGoogle Scholar
  11. 11.
    Roseman, A.: Findema fast, efficient program for automatic selection of particles from electron micrographs. Journal of Structural Biology 145(1), 91–99 (2004)CrossRefGoogle Scholar
  12. 12.
    Sigworth, F.J.: Classical detection theory and the cryo-em particle selection problem. Journal of Structural Biology 145(1), 111–122 (2004)CrossRefGoogle Scholar
  13. 13.
    Orlova, E.V., Dube, P., Harris, J.R., Beckman, E., Zemlin, F., Markl, J., van Heel, M.: Structure of keyhole limpet hemocyanin type 1 (klh1) at 15 å resolution by electron cryomicroscopy and angular reconstitution. Journal of Molecular Biology 271(3), 417–437 (1997)CrossRefGoogle Scholar
  14. 14.
    Kumar, V., Heikkonen, J., Engelhardt, P., Kaski, K.: Robust filtering and particle picking in micrograph images towards 3d reconstruction of purified proteins with cryo-electron microscopy. Journal of Structural Biology 145(1), 41–51 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yu Chen
    • 1
    • 2
  • Fei Ren
    • 1
  • Xiaohua Wan
    • 1
  • Xuan Wang
    • 3
  • Fa Zhang
    • 1
  1. 1.Key Lab. of Intelligent Information Processing, and Advanced Computing Research Lab., Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of Sciences BeijingChina
  3. 3.Yanshan UniversityChina

Personalised recommendations