DocumentCode :
55156
Title :
An Assembly Automation Approach to Alignment of Noncircular Projections in Electron Microscopy
Author :
Wooram Park ; Chirikjian, Gregory S.
Author_Institution :
Dept. of Mech. Eng., Univ. of Texas at Dallas, Richardson, TX, USA
Volume :
11
Issue :
3
fYear :
2014
fDate :
Jul-14
Firstpage :
668
Lastpage :
679
Abstract :
In single-particle electron microscopy (EM), multiple micrographs of identical macromolecular structures or complexes are taken from various viewing angles to obtain a 3D reconstruction. A high-quality EM reconstruction typically requires several thousand to several million images. Therefore, an automated pipeline for performing computations on many images becomes indispensable. In this paper, we propose a modified cross-correlation method to align a large number of images from the same class in single-particle electron microscopy of highly nonspherical structures, and show how this method fits into a larger automated pipeline for the discovery of 3D structures. Our modification uses a probability density in full planar position and orientation, akin to the pose densities used in Simultaneous Localization and Mapping (SLAM) and Assembly Automation. Using this alignment and a subsequent averaging process, high signal-to-noise ratio (SNR) images representing each class of viewing angles are obtained for reconstruction algorithms. In the proposed method, first we coarsely align projection images, and then realign the resulting images using the cross correlation (CC) method. The coarse alignment is obtained by matching the centers of mass and the principal axes of the images. The distribution of misalignment in this coarse alignment is estimated using the statistical properties of the additive background noise. As a consequence, the search space for realignment in the CC method is reduced. Additionally, in order to overcome the false peak problems in the CC, we use artificially blurred images for the early stage of the iteration and segment the intermediate result from every iteration step. The proposed approach is demonstrated on synthetic noisy images of GroEL/ES.
Keywords :
SLAM (robots); correlation theory; electron microscopes; image matching; image reconstruction; image representation; image segmentation; iterative methods; physics computing; pose estimation; probability; statistical analysis; 3D reconstruction algorithm; 3D structure discovery; CC method; EM reconstruction; GroEL/ES; SLAM; additive background noise; artificially blurred image; assembly automation approach; automated pipeline; centers of mass matching; coarse alignment; cross correlation method; false peak problems; image alignment; image representation; image segmentation; iteration method; macromolecular structure; micrograph; misalignment distribution; noncircular projection alignment; nonspherical structure; planar orientation; planar position; pose density; probability density; projection image alignment; search space; signal to noise ratio; simultaneous localization and mapping; single particle electron microscopy; statistical property; subsequent averaging process; synthetic noisy image; viewing angle; Automation; Electron microscopy; Image reconstruction; Noise measurement; Signal to noise ratio; Simultaneous localization and mapping; Class averages; cross correlation (CC) algorithm; image alignment; single-particle electron microscopy (EM);
fLanguage :
English
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1545-5955
Type :
jour
DOI :
10.1109/TASE.2013.2295398
Filename :
6708465
Link To Document :
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