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Computational Alignment Methods: Application to Biological Real Samples

机译：计算对齐方法：应用于生物真实样本

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In this paper, we review alignment techniques based on two statistical methods and Fiducial Markers. The aim of this overview is to highlight the advantages and the disadvantages of each method and to enhance the accuracy of alignment assessment. We use real biological TEM tilt series in different modes, namely scanning TEM (STEM) mode and Energy Filtered TEM (EFTEM) mode. Image registration is the process of aligning two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors. Image registration is a crucial step in imaging problems where the valuable information is contained in more than one image. Accurate image alignment is needed for computing three-dimensional reconstructions from transmission electron microscope tilt series. Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation or mutual information metrics between adjacent views is used to align them. However, sequential pair wise correlation is prone to bias and the resulting alignment is not always optimal.
机译：在本文中，我们根据两个统计方法和基准标记审查对齐技术。概述的目的是突出各种方法的优点和缺点，并提高对准评估的准确性。我们使用不同模式的真实生物TEM TILT系列，即扫描TEM（Stew）模式和能量滤波TEM（EFTEM）模式。图像配准是从不同的视点和/或不同传感器对齐在不同时间的相同场景的两个或更多个图像的过程。图像配准是成像问题的重要步骤，其中有价值信息包含在多个图像中。从透射电子显微镜倾斜系列计算三维重建需要精确的图像对准。倾斜序列通常用于电子断层扫描，作为从二维投影收集三维信息的手段。遇到的常见问题是在三维重建之前的投影对齐。电流对准技术通常使用金粒子或图像推导标记以正确地对准图像。当这些标记不存在时，使用相邻视图之间的相关或相互信息度量来对准它们。然而，顺序对明智的相关性容易偏置并且所得到的对准并不总是最佳的。

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