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GA-Based Inference of Euler Angles for Single Particle Analysis

机译:基于GA的欧拉角度的推断进行单粒子分析

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Single particle analysis is one of the methods for structural studies of protein and macromolecules developed in image analysis on electron microscopy. Reconstructing 3D structure from microscope images is not an easy analysis because of the low resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures is automatically devised [6]. In this paper, we propose a new method for determining Euler angles of projections by applying Genetic Algorithms (i.e., GAs). We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.
机译:单颗粒分析是电子显微镜图像分析中蛋白质和大分子的结构研究的方法之一。由于图像的低分辨率和3D结构中的图像的定向信息,重建从显微镜图像的3D结构并不容易分析。为了提高分辨率,不同的投影是对齐,分类和平均值。推断这些图像的方向是难以重建3D结构的任务取决于研究人员的经验。但最近,自动设计了一种重建3D结构的方法[6]。在本文中,我们提出了一种通过应用遗传算法(即气)来确定突起的欧拉角度的新方法。我们经验证明,在计算时间和获得精度方面,所提出的方法已经改进了前一个。

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