A Modified Cross Correlation Algorithm for Reference-free Image Alignment of Non-Circular Projections in Single-Particle Electron Microscopy

TL;DR

This paper introduces a modified cross correlation algorithm that improves the alignment of non-circular projections in single-particle electron microscopy, especially under low signal-to-noise conditions, by combining coarse alignment, noise-based search space reduction, and image blurring techniques.

Contribution

The novel method combines coarse alignment with a reduced search space and image blurring to enhance alignment accuracy for non-spherical structures in electron microscopy.

Findings

Better alignment for low SNR images than classical methods

Effective reduction of false peaks in cross correlation

Improved accuracy in aligning non-circular projections

Abstract

In this paper we propose a modified cross correlation method to align images from the same class in single-particle electron microscopy of highly non-spherical structures. In this new method, First we coarsely align projection images, and then re-align 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 can be quantified based on the statistical properties of the additive background noise. As a consequence, the search space for re-alignment in the cross correlation method can be reduced to achieve better alignment. In order to overcome problems associated with false peaks in the cross correlations function, we use artificially blurred images for the early stage of the iterative cross correlation method and segment the intermediate class average from every iteration step. These two additional manipulations combined with the reduced search space size in the cross correlation method yield better alignments for low signal-to-noise ratio images than both classical cross correlation and maximum likelihood(ML) methods.