Local adaptive wiener filtering for class averaging in single particle reconstruction

Ali Abdollahzadeh*, Erman Acar, Sari Peltonen, Ulla Ruotsalainen

*Corresponding author for this work

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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.

Original languageEnglish
Title of host publicationImage Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings
EditorsPuneet Sharma, Filippo Maria Bianchi
PublisherSpringer Verlag
Pages233-244
Number of pages12
ISBN (Print)9783319591285
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event20th Scandinavian Conference on Image Analysis, SCIA 2017 - Tromso, Norway
Duration: 12 Jun 201714 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10270 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Scandinavian Conference on Image Analysis, SCIA 2017
CountryNorway
CityTromso
Period12/06/1714/06/17

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Keywords

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

Cite this

Abdollahzadeh, A., Acar, E., Peltonen, S., & Ruotsalainen, U. (2017). Local adaptive wiener filtering for class averaging in single particle reconstruction. In P. Sharma, & F. M. Bianchi (Eds.), Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings (pp. 233-244). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10270 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59129-2_20