Huang Zhong, Penczek Pawel A
Department of Biochemistry and Molecular Biology, The University of Texas--Houston Medical School, 6431 Fannin, MSB 6.218, Houston, TX 77030, USA.
J Struct Biol. 2004 Jan-Feb;145(1-2):29-40. doi: 10.1016/j.jsb.2003.11.004.
Template matching together with the comprehensive theory of image formation in electron microscope provides an optimal (in Bayesian sense) tool for solving one of the outstanding problems in single particle analysis, i.e., automatic selection of particle views from noisy micrograph fields. The method is based on the assumption that the reference three-dimensional structure is known and that the relevant parameters of the model of the image formation process can be estimated. In the first stage of the procedure, a set of possible particle views is generated using the available reference structure. The template images are constructed as linear combinations of available particle views using a clustering technique. Next, the micrograph noise characteristic is established using an automated contrast transfer function (CTF) estimation procedure. Finally, the CTF parameters calculated are used to construct a matched filter and correlation functions corresponding to the available template images are calculated. In order to alleviate the problem of the biased caused by varying image formation conditions, a decision making strategy based on the predicted distribution of correlation coefficients is proposed. It is demonstrated that due to the inclusion of CTF considerations, the template matching method performed very well in a broad range of microscopy conditions.
模板匹配与电子显微镜图像形成的综合理论相结合，为解决单颗粒分析中的一个突出问题提供了一种最优（从贝叶斯意义上讲）工具，即从噪声显微图像场中自动选择颗粒视图。该方法基于这样的假设：参考三维结构已知，并且图像形成过程模型的相关参数可以估计。在该过程的第一阶段，使用可用的参考结构生成一组可能的颗粒视图。使用聚类技术将模板图像构建为可用颗粒视图的线性组合。接下来，使用自动对比度传递函数（CTF）估计程序确定显微图像噪声特征。最后，将计算出的CTF参数用于构建匹配滤波器，并计算与可用模板图像对应的相关函数。为了缓解由变化的图像形成条件引起的偏差问题，提出了一种基于相关系数预测分布的决策策略。结果表明，由于考虑了CTF，模板匹配方法在广泛的显微镜条件下表现良好。
