A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering

Abstract

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.

SimCryoCluster: A Semantic Similarity Clustering Method of Cryo-EM Images by Adopting Contrastive Learning

“…The purpose of image alignment is to estimate the three parameters of alignment, namely the angle of rotation, and the translational movement in the direction of the x-axes and y-axes. Image rotation alignment and translation are also often used in time domain space, but in time domain space it is usually matched by rotation in a certain step, it takes multiple iterations to calculate the alignment parameters, and the result is an integer [33]. In frequency domain space, the calculation alignment parameters can be calculated directly without enumeration.…”

Cryo-Electron Microscopy Image Registration Using Projection Method for Fourier-Bessel Transform Computation

“…Классические методы основаны на поиске параметров совмещения (угол поворота и двумерный вектор смещения) в пространственной области, что в большинстве случаев представляет из себя вычислительно затратный итеративный поиск. Другая группа классических методов включает в себя поиск параметров совмещения в частотном пространстве и использует различные модификации преобразования Фурье [1, 5,6,7,8,9,10,11]. Помимо этого, за последние годы появились методы, которые решают задачу выравнивания совместно с задачей кластеризации [12,13].…”

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders

“…As a result of these exciting new developments, cryo-EM was selected as "Method of the Year 2015" by Nature Methods, and the 2017 Nobel Prize in Chemistry was awarded to Jacques Dubochet, Joachim Frank, and Richard Henderson for "developing cryo-electron microscopy (cryo-EM) for the high-resolution structure determination of biomolecules in solution". In recent years, cryo-EM single-particle 3D reconstruction technologies have been continuously improved and developed [14][15][16].…”