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.定量评估的统计标准 空间分辨率（光谱信噪比）;和快速 用于确定两个之间的比例因子的算法 显微照片 为了获得更高分辨率的三维重建 目前的目标是将大量的二十面体病毒联合收割机 在放大率和图像对比度上有轻微差异的显微照片。 一个目标，解决几何校正的问题，使我们 开发新的算法来估计和执行 所需的几何变换（缩放、平移和旋转） 具有最小失真量（最小二乘准则）。 我们 也致力于该问题的第二个反卷积方面，并且 开发了新的多道识别和反褶积技术 我们就能修正对比度转移的影响 显微镜的功能。 这些方法需要具备 同一标本的多个图像记录在不同程度的 散焦。

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. In order to obtain higher-resolution 3-D reconstructions of icosahedral viruses, a current goal is to combine a larger number of micrographs with slight disparities in magnification and image contrast. One objective, to address the issue of geometric corrections, 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, and 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.