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

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


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.


Particle selection Rotation invariant feature Correlation Divide and Conquer Cascade strategy 


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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

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