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Restoration of Uneven Illumination in Light Sheet Microscopy Images

Published online by Cambridge University Press:  20 June 2011

Mohammad Shorif Uddin ,
Hwee Kuan Lee ,
Stephan Preibisch  and
Pavel Tomancak
Mohammad Shorif Uddin
Affiliation:
Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, Singapore13867
Hwee Kuan Lee*
Affiliation:
Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, Singapore13867
Stephan Preibisch
Affiliation:
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
Pavel Tomancak
Affiliation:
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
*
Corresponding author. E-mail: [email protected]
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Abstract

Light microscopy images suffer from poor contrast due to light absorption and scattering by the media. The resulting decay in contrast varies exponentially across the image along the incident light path. Classical space invariant deconvolution approaches, while very effective in deblurring, are not designed for the restoration of uneven illumination in microscopy images. In this article, we present a modified radiative transfer theory approach to solve the contrast degradation problem of light sheet microscopy (LSM) images. We confirmed the effectiveness of our approach through simulation as well as real LSM images.

Keywords

digital image processingimage reconstruction-restorationmicroscopymathematical methods in physics
Type
Technology and Software Development Light and Confocal Microscopy
Information
Microscopy and Microanalysis , Volume 17 , Issue 4 , August 2011 , pp. 607 - 613
Copyright
Copyright © Microscopy Society of America 2011

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References

REFERENCES

Adiga, P.S.U. & Chaudhuri, B.B. (2001). Some efficient methods to correct confocal images for easy interpretation. Micron 32, 363–370.CrossRefGoogle Scholar
Agard, D.A. (1984). Optical sectioning microscopy: Cellular architecture in three dimensions. Ann Rev Biophys Bioeng 13, 191–219.CrossRefGoogle ScholarPubMed
Brakenhoff, G., Van der Voort, H.T., Van Spronson, E.A. & Nanninga, N. (1988). 3-dimensional imaging of biological structures by high resolution confocal scanning laser microscopy. Scanning Microscopy 2, 33–40.Google Scholar
Chandrasekhar, S. (1960). Radiative Transfer, Chesapeake, VA: Dove Publications.Google Scholar
Combettes, P.L. & Pesquet, J.C. (2004). Image restoration subject to a total variation constraint. IEEE T Image Process 13, 1213–1222.CrossRefGoogle ScholarPubMed
Geman, S. & Geman, G. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE T Pattern Anal 6, 721–741.CrossRefGoogle ScholarPubMed
Greger, K., Swoger, J. & Stelzer, E.H.K. (2007). Basic building units and properties of a fluorescence single plane illumination microscope. Rev Sci Instrum 78, 023705.CrossRefGoogle ScholarPubMed
Guan, Y.Q., Cai, Y.Y., Zhang, X., Lee, X.Y. & Opas, M. (2008). Adaptive correction technique for 3D reconstruction of fluorescence microscopy images. Microsc Res Techniq 71, 146–157.CrossRefGoogle ScholarPubMed
Huisken, J., Swoger, J.J., Del Bene, F., Wittbrodt, J. & Stelzer, E.H.K. (2004). Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305, 1007–1009.CrossRefGoogle ScholarPubMed
Keller, P.J., Pampaloni, F. & Stelzer, E.H.K. (2006). Life sciences require the third dimension. Curr Opin Cell Biol 18, 117–124.CrossRefGoogle ScholarPubMed
Kiefer, J. (1953). Sequential minimax search for a maximum. Proc Amer Math Soc 4(3), 502–506.CrossRefGoogle Scholar
Kundur, D. & Hatzinakos, D. (1996). Blind image deconvolution. IEEE Signal Proc Mag 13, 43–64.CrossRefGoogle Scholar
Lee, H.K., Uddin, M.S., Sankaran, S., Hariharan, S. & Ahmed, S. (2009). A field theoretical restoration methdod for images degraded by non-uniform light attenuation: An application for light microscopy. Opt Express 17, 11294–11308.CrossRefGoogle ScholarPubMed
Murphy, D.B. & Tanke, H.J. (1987). Fundamentals of Light Microscopy and Electronic Imaging. New York: Oxford University Press.Google Scholar
Narasimhan, S.G. & Nayar, S.K. (2000). Chromatic framework for vision in bad weather. Proceedings of International Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, June 2000, pp. 598–605.Google Scholar
Narasimhan, S.G. & Nayar, S.K. (2001). Removing weather effects from monochrome images. Proceedings of International Conference on Computer Vision and Pattern Recognition, Lihue, Hawaii, June 2001, pp. 186–193.Google Scholar
Narasimhan, S.G. & Nayar, S.K. (2002). Vision and the atmosphere. Int J Computer Vision 48, 233–254.CrossRefGoogle Scholar
Narasimhan, S.G. & Nayar, S.K. (2003a). Contrast restoration of weather degraded images. IEEE T Pattern Anal 25, 713–724.CrossRefGoogle Scholar
Narasimhan, S.G. & Nayar, S.K. (2003b). Polarization based vision through haze. Appl Opt 42, 511–525.Google Scholar
Oakley, J.P. & Satherley, B.L. (1998). Improving image quality in poor visibility conditions using a physical model for degradation. IEEE T Image Process 7, 167–179.CrossRefGoogle Scholar
Ploem, J.S. & Tanke, H.J. (2001). Introduction to Fluorescence Microscopy. New York: Wiley Liss, Inc.Google Scholar
Ritter, J.G., Veith, R., Siebrasse, J. & Kubitscheck, U. (2008). High-contrast single-particle tracking by selective focal plane illumination microscopy. Opt Express 16, 7142–7152.CrossRefGoogle ScholarPubMed
Rudin, L., Osher, S. & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268.CrossRefGoogle Scholar
Sarder, P. & Nehorai, A. (2006). Deconvolution methods for 3-D fluorescence microscopy images. IEEE Signal Process Mag 23, 32–45.CrossRefGoogle Scholar
Schechner, Y.Y. & Karpel, N. (2004). Clear underwater vision. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, pp. 536–543.Google Scholar
Sharkov, E.A. (2003). Passive Microwave Remote Sensing of the Earth. Chichester, UK: Praxis Publishing Ltd.Google Scholar
Shaw, P. (1994). Deconvolution in 3-D optical microscopy. Histochem J 26, 1573–6865.CrossRefGoogle ScholarPubMed
Tan, K. & Oakley, J.P. (2000). Enhancement of color images in poor visibility conditions. Proceedings of International Conference on Image Processing, Vancouver, BC, Canada, September 10–13, 2000, pp. 788–791. Washington, DC: IEEE Computer Society.Google Scholar
Tan, K. & Oakley, J.P. (2001). Physics based approach to color image enhancement in poor visibility conditions. J Opt Soc Am A 18, 2460–2467.CrossRefGoogle ScholarPubMed
Tikhonov, A.N. & Arsenin, V.Y. (1977). Solutions of Ill-Posed Problems. New York: Wiley.Google Scholar
Van der Kempen, G.M.P., van Vliet, L.J, Verveer, P.J. & Van der Voort, H.T.M. (1997). A quantitative comparison of image restoration methods for confocal microscopy. J Microsc 185, 354–365.CrossRefGoogle Scholar
Verveer, P.J., Swoger, J., Pampaloni, F., Greger, K., Marcello, M. & Stelzer, E.H.K. (2007). High-resolution three dimensional imaging of large specimens with light sheet-based microscopy. Nat Methods 4, 311–313.CrossRefGoogle ScholarPubMed
Zimmermann, T.J., Rietdorf, J., Girod, T.A., Georget, J. & Pepperkok, R. (2002). Spectral imaging and linear un-mixing enables improved FRET efficiency with a novel GFP2–YFP FRET pair. FEBS Lett 531, 245–249.CrossRefGoogle ScholarPubMed