A Monte Carlo framework for denoising and missing wedge reconstruction in cryo-electron tomography

Abstract : We propose a statistical method to address an important issue in cryo electron to-mography 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 tomogram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated tomogram. The artifact compensation is achieved by filling up the MW with meaningful information. The method can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification. Results are presented for both synthetic and experimental data.
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https://hal.inria.fr/hal-01961938
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Submitted on : Thursday, December 20, 2018 - 11:53:02 AM
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Emmanuel Moebel, Charles Kervrann. A Monte Carlo framework for denoising and missing wedge reconstruction in cryo-electron tomography. Patch-MI 2018 - 4th International Workshop on Patch-based Techniques in Medical Imaging, Sep 2018, Grenade, Spain. pp.28-35, ⟨10.1007/978-3-030-00500-9_4⟩. ⟨hal-01961938⟩

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