Local adaptive wiener filtering for class averaging in single particle reconstruction

Ali Abdollahzadeh, Erman Acar, Sari Peltonen, Ulla Ruotsalainen

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

    Abstrakti

    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.

    AlkuperäiskieliEnglanti
    OtsikkoImage Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings
    KustantajaSpringer Verlag
    Sivut233-244
    Sivumäärä12
    ISBN (painettu)9783319591285
    DOI - pysyväislinkit
    TilaJulkaistu - 2017
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaSCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS -
    Kesto: 1 tammik. 1900 → …

    Julkaisusarja

    NimiLecture Notes in Computer Science
    Vuosikerta10270
    ISSN (painettu)0302-9743
    ISSN (elektroninen)1611-3349

    Conference

    ConferenceSCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS
    Ajanjakso1/01/00 → …

    Julkaisufoorumi-taso

    • Jufo-taso 1

    !!ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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