Literature DB >> 20802209

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

J R Bilbao-Castro1, C O S Sorzano, I García, J J Fernández.   

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/

Mesh:

Year:  2010        PMID: 20802209     DOI: 10.1093/bioinformatics/btq496

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  1 in total

Review 1.  Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities.

Authors:  Feng Jinchao; Shahzad Ahmed; Muhammad Yaqub; Kaleem Arshid; Wenqian Zhang; Muhammad Zubair Nawaz; Tariq Mahmood
Journal:  Comput Math Methods Med       Date:  2022-06-16       Impact factor: 2.809

  1 in total

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