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Detalle de: XMSF: Structure-preserving noise reduction and pre-segmentation in microscope tomography

Title XMSF: Structure-preserving noise reduction and pre-segmentation in microscope tomography
Abstract

Summary: 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. Availability: http://sites.google.com/site/xmsfilter/ Contact: jrbcast@ace.ual.es

Apa Reference Bilbao-Castro, J.R., Sorzano, C.O.S., García, I. & Fernández, J.J. (2010). XMSF: Structure-preserving noise reduction and pre-segmentation in microscope tomography. Bioinformatics, 26(21), 2786-2787. 10.1093/bioinformatics/btq496
Doi 10.1093/bioinformatics/btq496
Publication Date 27 Aug 2010
Publication Type Article
Researcher Jose-Jesus Fernandez
Ontology Experimental structure determination, Parallel Computing, Image analysis
Research Groups
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