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Photobleaching/Photoblinking Differential Equation Model for Fluorescence Microscopy Imaging

Published online by Cambridge University Press:  02 September 2013

J. Miguel Sanches*
Affiliation:
Institute for Systems and Robotics, Av. Rovisco Pais, Torre Norte, 1049-001, Lisboa, Portugal Department of Bioengineering, Lisbon Institute of Technology, University of Lisbon, Portugal
Isabel Rodrigues
Affiliation:
Institute for Systems and Robotics, Av. Rovisco Pais, Torre Norte, 1049-001, Lisboa, Portugal Instituto Superior de Engenharia de Lisboa-ADEETC, R. Conselheiro Emídio Navarro, 1, Ed. 5, 1959-007, Lisboa, Portugal
*
*Corresponding author. E-mail: jmrs@ist.utl.pt
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Abstract

Fluorescence images present low signal-to-noise ratio (SNR), are corrupted by a type of multiplicative noise with Poisson distribution, and are affected by a time intensity decay due to photoblinking and photobleaching (PBPB) effects. The noise and the PBPB effects together make long-term biological observation very difficult. Here, a theoretical model based on the underlying quantum mechanic physics theory of the observation process associated with this type of image is presented and the common empirical weighted sum of two decaying exponentials is derived from the model. Improvement in the SNR obtained in denoising when the proposed method is used is particularly important in the last images of the sequence where temporal correlation is used to recover information that is sometimes faded and therefore useless from a visual inspection point of view. The proposed PBPB model is included in a Bayesian denoising algorithm previously proposed by the authors. Experiments with synthetic and real data are presented to validate the PBPB model and to illustrate the effectiveness of the model in denoising and reconstruction results.

Type
Portuguese Society for Microscopy
Copyright
Copyright © Microscopy Society of America 2013 

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References

Berger, J. (1985). Statistical Decision Theory and Bayesian Analysis. New York: Springer-Verlag.Google Scholar
Bertero, M. & Boccacci, P. (1998). Introduction to Inverse Problems in Imaging. Bristol, UK: Institute of Physics Publishing.Google Scholar
Besag, J. (1986). On the statistical analysis of dirty pictures. J R Statist Soc B 48(3), 259302.Google Scholar
Brokmann, X., Hermier, J.-P., Messin, G., Desbiolles, P., Bouchaud, J.-P. & Dahan, M. (2003). Statistical aging and nonergodicity in the fluorescence of single nanocrystals. Phys Rev Lett 90(12), 120601-1. Google Scholar
Buades, A., Coll, B. & Morel, J.M. (2005). A review of image denoising algorithms, with a new one. SIAM Multiscale Model Simul 4(2), 490530.CrossRefGoogle Scholar
Csiszáer, I. (1991). Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. Ann Stat 19(4), 20322066.Google Scholar
Dey, N., Blanc-Féraud, L., Zimmer, C., Kam, Z., Olivo-Marin, J.-C. & Zerubia, J. (2006). Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microsc Res Tech 69(4), 260266.Google Scholar
Didier, P., Guidoni, L. & Bardou, F. (2005). Infinite average lifetime of an unstable bright state in the green fluorescent protein. Phys Rev Lett 95(9), 090602/1–4.Google Scholar
Dupé, F.-X., Fadili, J.M. & Starck, J.-L. (2008). Deconvolution of confocal microscopy images using proximal iteration and sparse representations. In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14–17, 2008 , pp. 736739. Paris, France: IEEE.Google Scholar
Dupé, F.-X., Fadili, J.M. & Starck, J.-L. (2009). A proximal iteration for deconvolving Poisson noisy images using sparse representations. IEEE Trans Image Process 18(2), 310321.Google Scholar
Gavrilyuk, S., Polyutov, S., Jha, P.C., Rinkevicius, Z., Ågren, H. & Gel'mukhanov, F. (2007). Many-photon dynamics of photobleaching. J Phys Chem 111(47), 1196111975.CrossRefGoogle ScholarPubMed
Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6, 721741.Google Scholar
Jackson, D.A., Iborra, F.J., Manders, E.M. & Cook, P.R. (1998). Numbers and organization of RNA polymerases, nascent transcripts, and transcription units in hela nuclei. Mol Biol Cell 9, 15231536.CrossRefGoogle ScholarPubMed
Kempen, G.M.P., Vliet, L.J. & Verveer, P.J. (1997). Application of image restoration methods for confocal fluorescence microscopy. In Proc SPIE, 3-D Microscopy: Image Acquisition and Processing IV, vol. 2984, Wilson, T., Cogswell, C.J. & Conchello, J.-A. (Eds.), pp. 114124. San Jose, CA: SPIE.Google Scholar
Lichtman, J.W. & Conchello, J.-A. (2005). Fluorescence microscopy. Nat Methods 2, 910919.CrossRefGoogle ScholarPubMed
Molenaar, C., Abdulle, A., Gena, A., Tanke, H.J. & Dirks, R.W. (2004). Apoly(a)+ RNAs roam the cell nucleus and pass through speckle domains in transcriptionally active and inactive cells. J Cell Biol 165, 191202.Google Scholar
Moon, T.K. & Stirling, W.C. (2000). Mathematical Methods and Algorithms for Signal Processing. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Rino, J., Braga, J., Henriques, R. & Carmo-Fonseca, M. (2009). Frontiers in fluorescence microscopy. Int J Dev Biol 53, 15691579.CrossRefGoogle ScholarPubMed
Rodrigues, I. & Sanches, J. (2010a). Photoblinking/photobleaching differential equation for intensity decay of fluorescence microscopy images, IEEE International Symposium on Biomedical Imaging (ISBI), April 14–17, Rotterdam, the Netherlands. Google Scholar
Rodrigues, I. & Sanches, J. (2010b). Convex total variation denoising of Poisson fluorescence confocal images with anisotropic filtering. IEEE Trans Image Process 20(1), 146160.CrossRefGoogle ScholarPubMed
Rodríguez, P. & Wohlberg, B. (2009). Efficient minimization method for a generalized total variation functional. IEEE Trans Image Process 18(2), 322332.Google Scholar
Sanches, J.M. & Marques, J.S. (2003). Joint image registration and volume reconstruction for 3D ultrasound. Pattern Recogn Lett 24(4-5), 791800.Google Scholar
Schuster, J., Brabandt, J. & Borczyskowski, C. (2007). Discrimination of photoblinking and photobleaching on the single molecule level. J Lumin 127(1), 224229.Google Scholar
Tomasi, C. & Manduchi, R. (1998). Bilateral filtering for gray and color images. In ICCV '98: Proceedings of the Sixth International Conference on Computer Vision, pp. 839846, Washington, DC: IEEE Computer Society.Google Scholar
Underwood, R. (2007). Frap and Flip, Photobleaching Technique to Reveal Cell Dynamics. Nikon. Available at http://nikon.com/products/instruments/bioscience-applications/application-notes/pdf/nikon_note_3nn06_8_07_lr.pdf.Google Scholar
Vargas, D.Y., Raj, A., Marras, S.A., Kramer, F.R. & Tyagi, S. (2005). Mechanism of mRNA transport in the nucleus. Proc Natl Acad Sci USA 102, 1700817013.CrossRefGoogle ScholarPubMed
Vicente, N.B., Zamboni, J.E.D., Adur, J.F., Paravani, E.V. & Casco, V.H. (2007). Photobleaching correction in fluorescence microscopy images. J Phys Condens Matter 90, 18.Google Scholar
Willett, R.M. (2006). Statistical analysis of photon-limited astronomical signals and images. In Statistical Challenges in Modern Astronomy IV (SCMA IV), Babu, G.J. & Feigelson, E.D. (Eds.), vol. 371, pp. 247264, Pennsylvania State University.Google Scholar
Willett, R.M. & Nowak, R.D. (2004a). Fast multiresolution photon-limited image reconstruction. In 2004 IEEE International Symposium on Biomedical Imaging: Nano to Macro, vol. 2, pp. 11921195, Arlington, VA.Google Scholar
Willett, R.M. & Nowak, R.D. (2004b). Fast, near-optimal, multiresolution estimation of Poisson signals and images. In EUSIPCO (XII European Signal Processing Conference), Vienna, Austria , pp. 10551058.Google Scholar
Zondervan, R., Kulzer, F., Kolchenko, M.A. & Orrit, M. (2004). Photobleaching of rhodamine 6g in poly(vinyl alcohol) at the ensemble and single-molecule levels. J Phys Chem A 108(10), 16571665.Google Scholar