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Learning Real Noise for Ultra-Low Dose Lung CT Denoising Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Monte Carlo Framework for Denoising and Missing Wedge Reconstruction in Cryo-electron Tomography

Authors : E. Moebel, C. Kervrann

Published in: Patch-Based Techniques in Medical Imaging

Publisher: Springer International Publishing

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Abstract

We propose 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 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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previous chapter MRI Denoising and Artefact Removal Using Self-Organizing Maps for Fast Global Block-Matching
next chapter Robust Supervoxel Matching Combining Mid-Level Spectral and Context-Rich Features
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Metadata
Title
A Monte Carlo Framework for Denoising and Missing Wedge Reconstruction in Cryo-electron Tomography
Authors
E. Moebel
C. Kervrann
Copyright Year
2018
Book
Patch-Based Techniques in Medical Imaging

Print ISBN: 978-3-030-00499-6

Electronic ISBN: 978-3-030-00500-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00500-9_4

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