2 research outputs found
Mahalanobis Distance for Class Averaging of Cryo-EM Images
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.Comment: Final version accepted to the 14th IEEE International Symposium on
Biomedical Imaging (ISBI 2017
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MAHALANOBIS DISTANCE FOR CLASS AVERAGING OF CRYO-EM IMAGES
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