PROCESSING OF HIGH RESOLUTION ELECTRON MICROGRAPHS

多年来，我们已经开发了几种计算技术， 生物样品电镜组的统计分析 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），离群值 检测方案和聚类算法，以及统计 空间分辨率（光谱）定量评估标准 信噪比）。 已经更详细地考虑的一个方面是以下问题： 校正显微照片之间的放大倍率不匹配， 改变成像条件。 动机是能够获得 通过结合高分辨率的20面体病毒三维重建 大量的显微照片，放大倍数略有差异。 为此，我们推导出一种快速迭代算法， 通过匹配辅助一维函数的比例因子 （径向自相关）计算每个显微照片。我们还 开发了一种基于样条的算法，用于任意 缩放因子该程序是这样的，它最大限度地减少损失的 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.