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，定量的统计标准， 空间分辨率（光谱信噪比）的评估， 以及用于确定缩放因子的快速算法 两个显微镜之间。 目前的目标是将大量的显微照片与联合收割机结合起来， 放大率和图像对比度的微小差异，以便 获得更高分辨率的二十面体病毒三维重建。 目标之一是解决几何校正问题。 这 这项任务使我们开发了新的算法， 执行所需的几何变换（缩放， 平移和旋转），具有最小的失真量（最小 平方准则）。 我们还在进行第二次反卷积 问题的方面。 为此，我们开发了新的 多通道识别和去卷积技术， 允许我们校正对比度传递函数的影响 的显微镜。 这些方法需要多个 在不同程度的散焦下记录的相同样本的图像。

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), outlier detection schemes, statistical criteria for the quantitative assessment of spatial resolution (spectral signal-to-noise ratios), and a fast algorithm for the determination of the scaling factors between two micrographs. A current goal is to combine a larger number of micrographs with slight disparities in magnification and image contrast, in order to obtain higher-resolution 3-D reconstructions of icosahedral viruses. One objective was to address the issue of geometric corrections. This task led us to the development of new algorithms for estimating and performing the required geometrical transformations (scaling, translation, and rotation) with the least amount of distortion (least squares criterion). We are also working on the second deconvolution aspect of the problem. For this purpose, we have developed new multichannel identification and deconvolution techniques that should allow us to correct for the effect of the contrast transfer function of the microscope. These methods require the availability of multiple images of the same specimen recorded at varying degrees of defocus.