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Local adaptive wiener filtering for class averaging in single particle reconstruction

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationImage Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings
PublisherSpringer Verlag
Pages233-244
Number of pages12
ISBN (Print)9783319591285
DOIs
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventScandinavian Conference on Image Analysis -
Duration: 1 Jan 1900 → …

Publication series

NameLecture Notes in Computer Science
Volume10270
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceScandinavian Conference on Image Analysis
Period1/01/00 → …

Abstract

In cryo-electron microscopy (cryo-EM), the Wiener filter is the optimal operation – in the least-squares sense – of merging a set of aligned low signal-to-noise ratio (SNR) micrographs to obtain a class average image with higher SNR. However, the condition for the optimal behavior of the Wiener filter is that the signal of interest shows stationary characteristic thoroughly, which cannot always be satisfied. In this paper, we propose substituting the conventional Wiener filter, which encompasses the whole image for denoising, with its local adaptive implementation, which denoises the signal locally. We compare our proposed local adaptive Wiener filter (LA-Wiener filter) with the conventional class averaging method using a simulated dataset and an experimental cryo-EM dataset. The visual and numerical analyses of the results indicate that LA-Wiener filter is superior to the conventional approach in single particle reconstruction (SPR) applications.

Keywords

  • Class averaging, Electron microscopy, Local adaptive Wiener filter, Single particle reconstruction, Spectral signal-to-noise ratio

Publication forum classification

Field of science, Statistics Finland