共 22 条
The discriminative bilateral filter: An enhanced denoising filter for electron microscopy data
被引:25
作者:
Pantelic, Radosav S.
Rothnagel, Rosalba
Huang, Chang-Yi
Muller, David
Woolford, David
Landsberg, Michael J.
McDowall, Alasdair
Pailthorpe, Bernard
Young, Paul R.
Banks, Jasmine
Hankamer, Ben
[1
]
Ericksson, Geoffery
机构:
[1] Univ Queensland, Inst Mol Biosci, Brisbane, Qld 4072, Australia
[2] Univ Queensland, Sch Mol & Microbial Sci, Brisbane, Qld 4072, Australia
[3] Univ Queensland, Ctr Microscopy & Microanal, Brisbane, Qld 4072, Australia
[4] Univ Queensland, Sch Phys Sci, Brisbane, Qld 4072, Australia
[5] Univ Queensland, Adv Computat Modelling Ctr, Brisbane, Qld 4072, Australia
[6] Univ Queensland, Queensland Brain Inst, Brisbane, Qld 4072, Australia
基金:
澳大利亚研究理事会;
关键词:
electron microscopy;
electron cryo-microscopy;
cryo-electron microscopy;
single particle analysis;
tomography;
image;
impulse noise reduction;
denoising;
filter;
bilateral;
anisotropic;
D O I:
10.1016/j.jsb.2006.03.030
中图分类号:
Q5 [生物化学];
Q7 [分子生物学];
学科分类号:
071010 ;
081704 ;
摘要:
Advances in three-dimensional (313) electron microscopy (EM) and image processing are providing considerable improvements in the resolution of subcellular volumes, macromolecular assemblies and individual proteins. However, the recovery of high-frequency information from biological samples is hindered by specimen sensitivity to beam damage. Low dose electron cryo-microscopy conditions afford reduced beam damage but typically yield images with reduced contrast and low signal-to-noise ratios (SNRs). Here, we describe the properties of a new discriminative bilateral (DBL) filter that is based upon the bilateral filter implementation of Jiang et al. (Jiang, W., Baker, M.L., Wu, Q., Bajaj, C., Chin, W., 2003. Applications of a bilateral denoising filter in biological electron microscopy. J. Struc. Biol. 128, 82-97.). In contrast to the latter, the DBL filter can distinguish between object edges and high-frequency noise pixels through the use of an additional photometric exclusion function. As a result, high frequency noise pixels are smoothed, yet object edge detail is preserved. In the present study, we show that the DBL filter effectively reduces noise in low SNR single particle data as well as cellular tomograms of stained plastic sections. The properties of the DBL filter are discussed in terms of its usefulness for single particle analysis and for pre-processing cellular tomograms ahead of image segmentation. (c) 2006 Elsevier Inc. All rights reserved.
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页码:395 / 408
页数:14
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