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XMSF: Structure-preserving noise reduction and pre-segmentation in microscope tomography
Author(s) -
J.R. Bilbao-Castro,
Carlos Óscar S. Sorzano,
I. García,
JoséJesús Fernández
Publication year - 2010
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btq496
Subject(s) - segmentation , noise reduction , reduction (mathematics) , tomography , noise (video) , computer science , artificial intelligence , microscope , computer vision , microscopy , pattern recognition (psychology) , mathematics , radiology , medicine , optics , image (mathematics) , physics , geometry
Interpretation of electron tomograms is difficult due to the high noise levels. Thus, denoising techniques are needed to improve the signal-to-noise ratio. XMSF (Microscopy Mean Shift Filtering) is a fast, user-friendly application that succeeds in filtering noise while preserving the structures of interest. It is based on the extension to 3D of a method widely applied in other image processing fields under very different scenarios. XMSF has been tested for a variety of tomograms, showing a great potential to become a state-of-the-art filtering program in electron tomography. Applied iteratively, the algorithm yields pre-segmented volumes facilitating posterior segmentation tasks. Moreover, execution times remain low thanks to parallel computing techniques to exploit current multicore computers.

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