A Monte Carlo framework for noise removal and missing wedge restoration in cryo-electron tomography

Abstract : In this paper, we describe a statistical method to address an important issue in cryo-electron tomography image analysis: reduction of a high amount of noise and artifacts due to the presence of a missing wedge (MW) in the spectral domain. The method takes as an input a 3D tomo-gram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated volume. The artifact compensation is achieved by filling up the MW with meaningful information. To address this inverse problem, we compute a Minimum Mean Square Error (MMSE) estimator of the uncorrupted image. The underlying high-dimensional integral is computed by applying a dedicated Markov Chain Monte-Carlo (MCMC) sampling procedure based on the Metropolis-Hasting (MH) algorithm. The proposed computational method can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification of macromolecules. Results are presented for both synthetic data and real 3D cryo-electron images.
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Submitted on : Saturday, December 29, 2018 - 7:10:16 PM
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  • HAL Id : hal-01966821, version 1

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Emmanuel Moebel, Charles Kervrann. A Monte Carlo framework for noise removal and missing wedge restoration in cryo-electron tomography. 2018. ⟨hal-01966821⟩

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