A maximum-likelihood approach to single-particle image refinement
A maximum-likelihood approach to single-particle image refinement
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DOI：
10.1006/jsbi.1998.4014
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发表时间：
1998-01-01
影响因子：
3
通讯作者：
Sigworth, FJ
中科院分区：
文献类型：
--
作者：
Sigworth, FJ
在低信噪比和小颗粒尺寸下，单粒子图像的对准失败，因为噪声会在用于对准的互相关函数中产生假峰。描述了一种用于二维对准问题的最大似然方法，该方法允许从非常噪声的图像的大数据集估计底层结构。该算法不是为每个图像寻找最优对齐，而是对图像的所有可能的面内旋转和平移形成加权和。作为图像变换的概率的加权因子被计算为互相关函数的指数。利用该算法构建了模拟数据集，并对其进行处理。结果表明，对起始基准的选择的敏感度大大降低，并且能够从具有非常低的信噪比的大数据集中恢复结构。(C)1998年学术出版社。
The alignment of single-particle images fails at low signal-to-noise ratios and small particle sizes, because noise produces false peaks in the crosscorrelation function used for alignment. A maximum-likelihood approach to the two-dimensional alignment problem is described which allows the underlying structure to be estimated from large data sets of very noisy images. Instead of finding the optimum alignment for each image, the algorithm forms a weighted sum over all possible in-plane rotations and translations of the image. The weighting factors, which are the probabilities of the image transformations, are computed as the exponential of a cross-correlation function. Simulated data sets were constructed and processed by the algorithm. The results demonstrate a greatly reduced sensitivity to the choice of a starting reference, and the ability to recover structures from large data sets having very low signal-to-noise ratios. (C) 1998 Academic Press.