Mahalanobis Distance for Class Averaging of Cryo-EM Images
This incremental improvement addresses noise reduction in cryo-EM image processing for structural biology.
The authors tackled the low signal-to-noise ratio in cryo-EM images by introducing a new Mahalanobis-like affinity measure to compare images across defocus groups, leading to state-of-the-art classification on synthetic datasets.
Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the SNR of the resulting averages, and is used for selecting particles from micrographs and for inspecting the particle images. We introduce a new affinity measure, akin to the Mahalanobis distance, to compare cryo-EM images belonging to different defocus groups. The new similarity measure is employed to detect similar images, thereby leading to an improved algorithm for class averaging. We evaluate the performance of the proposed class averaging procedure on synthetic datasets, obtaining state of the art classification.