PROCESSING OF HIGH RESOLUTION ELECTRON MICROGRAPHS

多年来，我们开发了几种计算技术，用于 生物电子显微照片组的统计分析 大分子。这些方法包括各种类型的阶乘 分析(对应分析、主成分分析)、异常值 检测方案，以及聚类算法，以及统计 空间分辨率定量评估标准(光谱 信噪比)。 更详细地考虑的一个方面是 更正缩微图之间的放大不匹配，这是由于 变化的成像条件。其动机是能够获得 组合重建二十面体病毒的高分辨率三维重建 放大倍数略有不同的大量显微照片。 为此，我们推导了一种快速迭代算法，该算法可以确定 一维辅助函数匹配的标度因子 (径向自相关)为每个显微图像计算。我们还有 提出了一种基于样条法的任意比例转换算法 比例因子。该程序是这样的，它将损失降至最低 信息。

Over the years, we have developed several computational techniques for the statistical analysis of sets of electron micrographs of biological macromolecules. These methods include various types of factorial analysis (correspondence analysis, principal components), an outlier detection scheme, and clustering algorithms, as well as a statistical criterion for quantitative assessment of spatial resolution (spectral signal-to-noise ratio). An aspect that has been considered in more detail is the problem of correcting for magnification mismatches between micrographs due to varying imaging conditions. The motivation is to be able to obtain higher-resolution 3-D reconstructions of icosahedral viruses by combining a larger number of micrographs with slight disparities in magnification. For this purpose, we derived a fast iterative algorithm that determines the scaling factors by matching auxiliary one-dimensional functions (radial autocorrelation) computed for each micrograph. We have also developed a spline-based algorithm for scale conversion with an arbitrary scaling factor. The procedure is such that it minimizes the loss of information.