Markov random field based automatic image alignment for electron tomography

被引:105
作者
Amat, Fernando [1 ]
Moussavi, Farshid [1 ]
Comolli, Luis R. [2 ]
Elidan, Gal [3 ]
Downing, Kenneth H. [2 ]
Horowitz, Mark [1 ]
机构
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[2] Lawrence Berkeley Natl Lab, Div Life Sci, Berkeley, CA USA
[3] Lawrence Berkeley Natl Lab, Dept Comp Sci, Berkeley, CA USA
基金
美国国家卫生研究院;
关键词
cryo electron microscopy; correspondence; probabilistic inference; tomography; Markov random fields; alignment; projection model estimation;
D O I
10.1016/j.jsb.2007.07.007
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. (C) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:260 / 275
页数:16
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