Markov random field based automatic image alignment for electron tomography

Fernando Amat, Farshid Moussavi, Luis R. Comolli, Gal Elidan, Kenneth H. Downing, Mark Horowitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

Original languageAmerican English
Pages (from-to)260-275
Number of pages16
JournalJournal of Structural Biology
Issue number3
StatePublished - Mar 2008
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Funding Information:
We also thank the National Center for Microscopy and Imaging Research (NCMIR) at UCSD for all the beta testing on RAPTOR and the plastic datasets. Also many thanks to Carolyn Larabell and her team at the National Center for X-ray Tomography (NCXT) for the X-ray datasets. They are funded by the National Center for Research Resources of the National Institutes of Health (P41 RR019664-02). We thank Grant Jensen’s lab in Caltech for cryo-EM datasets using automatic acquisition software , and Grant Bowman and Lucy Shapiro for the use of their unpublished Caulobacter image in Fig. 6 . Finally Fernando Amat thanks Argyris Zymnis of the Stanford University Electrical Engineering Department for his help in coding the optimization part in RAPTOR.


  • Alignment
  • Correspondence
  • Cryo electron microscopy
  • Markov Random Fields
  • Probabilistic inference
  • Projection model estimation
  • Tomography


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