Yu Zeyun, Bajaj Chandrajit
The Center of Computational Visualization, Department of Computer Sciences and Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
J Struct Biol. 2004 Jan-Feb;145(1-2):168-80. doi: 10.1016/j.jsb.2003.10.027.
Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM images) is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a method for particle picking based on shape feature detection. Two fundamental concepts of computational geometry, namely, the distance transform and the Voronoi diagram, are used for detection of critical features as well as for accurate location of particles from the images or micrographs. Unlike the conventional template-matching methods, our approach detects the particles based on their boundary features instead of intensities. The geometric features derived from the boundaries provide an efficient way for locating particles quickly and accurately, which avoids a brute-force searching for the best position/orientation. Our approach is fully automatic and has been successfully applied to detect particles with approximately circular or rectangular shapes (e.g., KLH particles). Particle detection can be enhanced by multiple sets of parameters used in edge detection and/or by anisotropic filtering. We also discuss the extension of this approach to other types of particles with certain geometric features.
从冷冻电子显微镜（冷冻电镜图像）中准确自动地检测颗粒对于大型大分子结构的高分辨率重建非常重要。在本文中，我们提出了一种基于形状特征检测的颗粒挑选方法。计算几何的两个基本概念，即距离变换和Voronoi图，用于关键特征的检测以及从图像或显微照片中准确确定颗粒的位置。与传统的模板匹配方法不同，我们的方法基于颗粒的边界特征而非强度来检测颗粒。从边界导出的几何特征为快速准确地定位颗粒提供了一种有效方法，避免了对最佳位置/方向进行暴力搜索。我们的方法是完全自动的，并且已成功应用于检测近似圆形或矩形形状的颗粒（例如钥孔血蓝蛋白颗粒）。通过在边缘检测中使用多组参数和/或通过各向异性滤波可以增强颗粒检测。我们还讨论了将此方法扩展到具有某些几何特征的其他类型颗粒的情况。
