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Comparison of Deconvolution Software: A User Point of View—Part 2
A. Griffa, N. Garin, D. Sage
G.I.T. Imaging & Microscopy, vol. 12, no. 3, pp. 41-43, August 2010.
Deconvolution is an image processing technique that restores the effective object representation [3] [4], allowing to improve images analysis steps such as segmentation [1] or colocalization study [2]. We performed several deconvolution tests on different kinds of datasets. The methodology has been reported in Part 1. Evaluation criteria and results are reported here.
References
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A. Chomik, A. Dieterlen, C. Xu, O. Haeberlé, J.J. Meyer, S. Jacquey, "Quantification in Optical Sectioning Microscopy: A Comparison of Some Deconvolution Algorithms in View of 3D Image Segmentation," Journal of Optics, vol. 28, no. 6, pp. 225-233, December 1997.
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L. Landmann, "Deconvolution Improves Colocalization Analysis of Multiple Fluorochromes in 3D Confocal Data Sets more than Filtering Techniques," Journal of Microscopy, vol. 208, no. 2, pp. 134-147, November 2002.
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J.-B. Sibarita, "Deconvolution Microscopy," Advances in Biochemical Engineering/Biotechnology, vol. 95, pp. 201-243, 2005.
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W. Wallace, L.H. Schaefer, J.R. Swedlow, "A Workingperson's Guide to Deconvolution in Light Microscopy," BioTechniques, vol. 31, no. 5, pp. 1076-1097, November 2001.
Please consult also the companion paper by A. Griffa, N. Garin, D. Sage, "Comparison of Deconvolution Software in 3D Microscopy: A User Point of View—Part 1," G.I.T. Imaging & Microscopy, vol. 12, no. 1, pp. 43-45, March 2010.
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@ARTICLE(http://bigwww.epfl.ch/publications/griffa1002.html,
AUTHOR="Griffa, A. and Garin, N. and Sage, D.",
TITLE="Comparison of Deconvolution Software: {A} User Point of
View---{P}art 2",
JOURNAL="G.I.T. Imaging \& Microscopy",
YEAR="2010",
volume="12",
number="3",
pages="41--43",
month="August",
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