Shatsky Maxim, Hall Richard J, Brenner Steven E, Glaeser Robert M
Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720-3102, USA.
J Struct Biol. 2009 Apr;166(1):67-78. doi: 10.1016/j.jsb.2008.12.008. Epub 2008 Dec 30.
We propose a feature-based image alignment method for single-particle electron microscopy that is able to accommodate various similarity scoring functions while efficiently sampling the two-dimensional transformational space. We use this image alignment method to evaluate the performance of a scoring function that is based on the Mutual Information (MI) of two images rather than one that is based on the cross-correlation function. We show that alignment using MI for the scoring function has far less model-dependent bias than is found with cross-correlation based alignment. We also demonstrate that MI improves the alignment of some types of heterogeneous data, provided that the signal-to-noise ratio is relatively high. These results indicate, therefore, that use of MI as the scoring function is well suited for the alignment of class-averages computed from single-particle images. Our method is tested on data from three model structures and one real dataset.
我们提出了一种用于单粒子电子显微镜的基于特征的图像对齐方法，该方法能够适应各种相似性评分函数，同时有效地对二维变换空间进行采样。我们使用这种图像对齐方法来评估基于两幅图像互信息（MI）的评分函数的性能，而不是基于互相关函数的评分函数。我们表明，使用MI作为评分函数进行对齐时，与基于互相关的对齐相比，模型依赖性偏差要小得多。我们还证明，只要信噪比相对较高，MI就能改善某些类型的异质数据的对齐。因此，这些结果表明，使用MI作为评分函数非常适合对从单粒子图像计算得到的类平均图像进行对齐。我们的方法在来自三个模型结构和一个真实数据集的数据上进行了测试。
