OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 23 — Nov. 18, 2013
  • pp: 27668–27681

Depth-variant deconvolution of 3D widefield fluorescence microscopy using the penalized maximum likelihood estimation method

Jeongtae Kim, Suhyeon An, Sohyun Ahn, and Boyoung Kim  »View Author Affiliations


Optics Express, Vol. 21, Issue 23, pp. 27668-27681 (2013)
http://dx.doi.org/10.1364/OE.21.027668


View Full Text Article

Enhanced HTML    Acrobat PDF (802 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We investigated the deconvolution of 3D widefield fluorescence microscopy using the penalized maximum likelihood estimation method and the depth-variant point spread function (DV-PSF). We build the DV-PSF by fitting a parameterized theoretical PSF model to an experimental microbead image. On the basis of the constructed DV-PSF, we restore the 3D widefield microscopy by minimizing an objective function consisting of a negative Poisson likelihood function and a total variation regularization function. In simulations and experiments, the proposed method showed better performance than existing methods.

© 2013 OSA

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(180.2520) Microscopy : Fluorescence microscopy

ToC Category:
Image Processing

History
Original Manuscript: September 6, 2013
Revised Manuscript: October 22, 2013
Manuscript Accepted: October 27, 2013
Published: November 4, 2013

Virtual Issues
Vol. 9, Iss. 1 Virtual Journal for Biomedical Optics

Citation
Jeongtae Kim, Suhyeon An, Sohyun Ahn, and Boyoung Kim, "Depth-variant deconvolution of 3D widefield fluorescence microscopy using the penalized maximum likelihood estimation method," Opt. Express 21, 27668-27681 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-23-27668


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. J. G. McNally, T. Karpova, J. Cooper, and J. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19, 373–385 (1999). [CrossRef] [PubMed]
  2. P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Proc. Mag.23, 32–45 (2006). [CrossRef]
  3. C. Preza and J. A. Conchello, “Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy,” J. Opt. Soc. Am. A21, 1593–1601 (2004). [CrossRef]
  4. J. Shaevitz and D. Fletcher, “Enhanced three-dimensional deconvolution microscopy using a measured depth-varying point-spread function,” J. Opt. Soc. Am. A24, 2622–2627 (2007). [CrossRef]
  5. J. G. McNally, C. Preza, J. A. Conchello, and L. J. Thomas, “Artifacts in computational optical-sectioning microscopy,” J. Opt. Soc. Am. A11, 1056–1067 (1994). [CrossRef]
  6. S. Joshi and M. I. Miller, “Maximum a posteriori estimation with Good’s roughness for three-dimensional optical-sectioning microscopy,” J. Opt. Soc. Am. A10, 1078–1085 (1993). [CrossRef] [PubMed]
  7. J. Markham and J. A. Conchello, “Parametric blind deconvolution: a robust method for the simultaneous estimation of image and blur,” J. Opt. Soc. Am. A16, 2377–2391 (1999). [CrossRef]
  8. J. Markham and J. A. Conchello, “Fast maximum-likelihood image-restoration algorithms for three-dimensional fluorescence microscopy,” J. Opt. Soc. Am. A18, 1062–1071 (2001). [CrossRef]
  9. C. Preza, M. I. Miller, J. Lewis, J. Thomas, and J. G. McNally, “Regularized linear method for reconstruction of three-dimensional microscopic objects from optical sections,” J. Opt. Soc. Am. A9, 219–228 (1992). [CrossRef] [PubMed]
  10. J. A. Conchello and J. G. McNally, “Fast regularization technique for expectation maximization algorithm for computational optical sectioning microscopy,” Proc. SPIE2655, 199–208 (1996) [CrossRef]
  11. P. Pankajakshan, B. Zhang, L. Blanc-Féraud, Z. Kam, J. Olivo-Marin, and J. Zerubia, “Blind deconvolution for diffraction-limited fluorescence microscopy,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2008), 740–743.
  12. P. J. Verveer and T. M. Jovin, “Efficient superresolution restoration algorithms using maximum a posteriori estimations with application to fluorescence microscopy,” J. Opt. Soc. Am. A14, 1696–1706 (1997). [CrossRef]
  13. S. F. Gibson and F. Lanni, “Experimental test of an analytical model of aberration in an oil-immersion objective lens used in three-dimensional light microscopy,” J. Opt. Soc. Am. A8, 1601–1613 (1991). [CrossRef]
  14. F. Aguet, D. Van De Ville, and M. Unser, “An accurate PSF model with few parameters for axially shift-variant deconvolution,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2008), 157–160.
  15. S. Ben Hadj, G. Blanc-Feraud, G. Aubert, and Engler, “Blind restoration of confocal microscopy images in presence of a depth-variant blur and poisson noise,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), 915–919.
  16. S. Bonettini, R. Zanella, and L. Zanni, “A scaled gradient projection method for constrained image deblurring,” Inverse Probl.25, 015002 (2009). [CrossRef]
  17. R. Zanella, G. Zanghirati, R. Cavicchioli, L. Zanni, P. Boccacci, M. Bertero, and G. Vicidomini, “Towards real-time image deconvolution: application to confocal and STED microscopy,” Sci. Rep.3, 2523(2013). [CrossRef] [PubMed]
  18. W. H. Richardson, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am.62, 55–59 (1972). [CrossRef]
  19. J. Fessler, “Image reconstruction: Algorithms and analysis,” Online preprint of book in preparation.
  20. S. Ben Hadj, L. Blanc-Feraud, E. Maalouf, B. Colicchio, and A. Dieterlen, “Depth-variant image restoration in 3D fluorescence microscopy: Two approaches under gaussian and poissonian noise conditions,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2012), 1671–1674.
  21. S. Ben Hadj and L. Blanc-Feraud, “Modeling and removing depth variant blur in 3D fluorescence microscopy,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2012), 689–692.
  22. N. Dey, L. Blanc-Feraud, C. Zimmer, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “A deconvolution method for confocal microscopy with total variation regularization,” in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, 2004), 1223–1226
  23. P. J. Green, “On use of the EM for penalized likelihood estimation,” J. R. Stat. Soc. B52, 443–452 (1990).
  24. A. De Pierro, “A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography,” IEEE Trans. Med. Imag.14, 132–137 (1995). [CrossRef]
  25. J. Fessler and A. Hero, “Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms,” IEEE Trans. Image Process.4, 1417–1429 (1995). [CrossRef] [PubMed]
  26. J. H. Chang, J. Anderson, and J. Votaw, “Regularized image reconstruction algorithms for positron emission tomography,” IEEE Trans. Med. Imag.23, 1165–1175 (2004). [CrossRef]
  27. L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Trans. Med. Imag.1, 113–122 (1982). [CrossRef]
  28. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C++ - The Art of Scientific Computing (Cambridge University, 2002).
  29. J. F. Aujol, “Some first-order algorithms for total variation based image restoration,” J. Math. Imaging Vis.34, 307–327 (2009). [CrossRef]
  30. S. Bonettini and V. Ruggiero, “An alternating extragradient method for total variation-based image restoration from poisson data,” Inverse Probl.27, 095001 (2011). [CrossRef]
  31. P. Huber, Robust Statistics (Wiley, 1974).
  32. J. Llacer and E. Veklerov, “Feasible images and practical stopping rules for iterative algorithms in emission tomography,” IEEE Trans. Med. Imag.8, 186–193 (1989). [CrossRef]
  33. N. Nguyen, P. Milanfar, and G. Golub, “Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement,” IEEE Trans. Image Process.10, 1299–1308 (2001). [CrossRef]
  34. F. Aguet, D. V. D. Ville, and M. Unser, “A maximum-likelihood formalism for sub-resolution axial localization of fluorescent nanoparticles,” Opt. Express13, 10503–10522 (2005). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Figures

Fig. 1 Fig. 2 Fig. 3
 
Fig. 4 Fig. 5
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited