一种改进的互相关算法用于单颗粒电子显微镜中非圆形投影的无参考图像对齐
A Modified Cross Correlation Algorithm for Reference-free Image Alignment of Non-Circular Projections in Single-Particle Electron Microscopy
AI总结：
针对单颗粒电子显微镜中高度非球形结构的图像对齐问题，提出一种改进的互相关方法，通过粗对齐和基于统计噪声的搜索空间缩减，结合人工模糊图像和中间类平均分割，在低信噪比下优于经典互相关和最大似然方法。
摘要：
本文提出了一种改进的互相关方法，用于对齐单颗粒电子显微镜中高度非球形结构的同一类图像。在该新方法中，首先对投影图像进行粗对齐，然后使用互相关（CC）方法重新对齐所得图像。粗对齐通过匹配图像的质心和主轴实现。基于加性背景噪声的统计特性，可以量化粗对齐中的未对准分布。因此，互相关方法中重新对齐的搜索空间可以缩小以实现更好的对齐。为了克服互相关函数中虚假峰值相关的问题，我们在迭代互相关方法的早期阶段使用人工模糊图像，并从每次迭代步骤中分割中间类平均。这两种额外的操作与互相关方法中缩小的搜索空间相结合，对于低信噪比图像，比经典互相关和最大似然（ML）方法产生更好的对齐效果。
英文摘要：
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.
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