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Local adaptive wiener filtering for class averaging in single particle reconstruction

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Local adaptive wiener filtering for class averaging in single particle reconstruction. / Abdollahzadeh, Ali; Acar, Erman; Peltonen, Sari; Ruotsalainen, Ulla.

Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings. Springer Verlag, 2017. p. 233-244 (Lecture Notes in Computer Science; Vol. 10270).

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

Harvard

Abdollahzadeh, A, Acar, E, Peltonen, S & Ruotsalainen, U 2017, Local adaptive wiener filtering for class averaging in single particle reconstruction. in Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings. Lecture Notes in Computer Science, vol. 10270, Springer Verlag, pp. 233-244, Scandinavian Conference on Image Analysis, 1/01/00. https://doi.org/10.1007/978-3-319-59129-2_20

APA

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

Vancouver

Abdollahzadeh A, Acar E, Peltonen S, Ruotsalainen U. Local adaptive wiener filtering for class averaging in single particle reconstruction. In Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings. Springer Verlag. 2017. p. 233-244. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-59129-2_20

Author

Abdollahzadeh, Ali ; Acar, Erman ; Peltonen, Sari ; Ruotsalainen, Ulla. / Local adaptive wiener filtering for class averaging in single particle reconstruction. Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings. Springer Verlag, 2017. pp. 233-244 (Lecture Notes in Computer Science).

Bibtex - Download

@inproceedings{aa09b0fc43d64c5c98afe05b37dd56d9,
title = "Local adaptive wiener filtering for class averaging in single particle reconstruction",
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",
author = "Ali Abdollahzadeh and Erman Acar and Sari Peltonen and Ulla Ruotsalainen",
note = "jufoid=62555",
year = "2017",
doi = "10.1007/978-3-319-59129-2_20",
language = "English",
isbn = "9783319591285",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "233--244",
booktitle = "Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings",
address = "Germany",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Local adaptive wiener filtering for class averaging in single particle reconstruction

AU - Abdollahzadeh, Ali

AU - Acar, Erman

AU - Peltonen, Sari

AU - Ruotsalainen, Ulla

N1 - jufoid=62555

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Class averaging

KW - Electron microscopy

KW - Local adaptive Wiener filter

KW - Single particle reconstruction

KW - Spectral signal-to-noise ratio

U2 - 10.1007/978-3-319-59129-2_20

DO - 10.1007/978-3-319-59129-2_20

M3 - Conference contribution

SN - 9783319591285

T3 - Lecture Notes in Computer Science

SP - 233

EP - 244

BT - Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings

PB - Springer Verlag

ER -