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1.
1.F. E. Boas and D. Fleischmann, “Evaluation of two iterative techniques for reducing metal artifacts in computed tomography,” Radiology 259, 894 (2011).
http://dx.doi.org/10.1148/radiol.11101782
2.
2.S. Van Aert, K. J. Batenburg, M. D. Rossell, R. Erni, and G. Van Tendeloo, “Three-dimensional atomic imaging of crystalline nanoparticles,” Nature 470, 374 (2011).
http://dx.doi.org/10.1038/nature09741
3.
3.Y. Wu, A. Ghitani, R. Christensen, A. Santella, Z. Du, G. Rondeau, Z. Bao, D. Coln-Ramos, and H. Shroff, Proc. Natl. Acad. Sciences 108, 17708 (2011).
http://dx.doi.org/10.1073/pnas.1108494108
4.
4.A. H. Voie, D. H. Burns, and F. A. Spelman, “Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens,” J. Microsc. 170, 229 (1993).
http://dx.doi.org/10.1111/j.1365-2818.1993.tb03346.x
5.
5.J. R. Monck et al., “Images Obtained by Optical Sectioning of fura-2 Loaded Mast Cells,” Journal of Cell Biology 116, 745 (1992).
http://dx.doi.org/10.1083/jcb.116.3.745
6.
6.M. Weinstein and K. R. Castleman, “Reconstructing 3-d specimens from 2-d section images,” Proceedings of the Society for Photo-Optical Instrument Engineering 26, 131 (1971).
7.
7.K. Yano and I Kumazawa, “A Modified Nearest Neighbor Method for Image Reconstruction in Fluorescence Microscopy,” Image Analysis and Recognition Lecture Notes in Computer Science 3212, 9 (2004).
http://dx.doi.org/10.1007/978-3-540-30126-4_2
8.
8.A. Erhardt, G. Zinser, D. Komitowski, and J. Bille, “Reconstructing 3-D light-microscopic images by digital image processing,” Applied Optics 24, 194 (1985).
http://dx.doi.org/10.1364/AO.24.000194
9.
9.A. Chomik, A. Dieterlen, C. Xu, O. Haeberle, J. J. Meyer, and S. Jacquey, “Quantification in optical sectioning microscopy: a comparison of some deconvolution algorithms in view of 3D image segmentation,” Journal of Optics 28, 225 (1997).
http://dx.doi.org/10.1088/0150-536X/28/6/001
10.
10.H. T. M. van der Voort and K.C. Strasters, “Restoration of confocal images for quantitative image analysis,” Jl. of Microsc. 179, 165 (1995).
http://dx.doi.org/10.1111/j.1365-2818.1995.tb03593.x
11.
11.P. A. Jansson, R. H. Hunt, and E. K. Plyler, “Resolution Enhancement of Spectra,” Journal of the Optical Society of America 60, 596 (1976).
http://dx.doi.org/10.1364/JOSA.60.000596
12.
12.D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three dimensions,” Methods in Cell Biology 30, 353 (1989).
http://dx.doi.org/10.1016/S0091-679X(08)60986-3
13.
13.W. A. Carrington, R. M. Lynch, E. D. Moore, G. Isenberg, K. E. Fogarty, and F. S. Fay, “Superresolution three-dimensional images of fluorescence in cells with minimal light exposure,” Science 268, 1483 (1995).
http://dx.doi.org/10.1126/science.7770772
14.
14.W. H. Richardson, J. Opt. Soc. Am. 62, 55 (1972).
http://dx.doi.org/10.1364/JOSA.62.000055
15.
15.L. B. Lucy, Astron. J. 79, 745 (1974).
http://dx.doi.org/10.1086/111605
16.
16.M. Bertero and P. Boccacci, Introduction of Inverse Problems in Imaging (IOP, London, 1998).
17.
17.S. Geman and D. Geman, IEEE Trans. Pattern Anal. Mach. Intell. 6, 721 (1984).
http://dx.doi.org/10.1109/TPAMI.1984.4767596
18.
18.Z. Zhou, R. M. Leahy, and J. Qi, IEEE Trans. Image Process. 6, 844 (1997).
http://dx.doi.org/10.1109/83.585235
19.
19.D. S. Lalush and B. M. W. Tsui, Phys. Med. Biol. 38, 729 (1993).
http://dx.doi.org/10.1088/0031-9155/38/6/007
20.
20.S. Joshi and M. I. Miller, Journal of the Optical Society of America 10, 1078 (1993).
http://dx.doi.org/10.1364/JOSAA.10.001078
21.
21.L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259 (1992).
http://dx.doi.org/10.1016/0167-2789(92)90242-F
22.
22.T. Lehman, C. Gonner, and K. Spitzer, “Survey: interpolation methods in medical image processing,” IEEE Trans. Medical Imaging 18, 1049 (1999).
http://dx.doi.org/10.1109/42.816070
23.
23.Mei-sen Pan, Xiao-li Yang, and Jing-tian Tang, “Research on Interpolation Methods in Medical Image Processing,” J Med Syst. 36, 777 (2012).
http://dx.doi.org/10.1007/s10916-010-9544-6
24.
24.J. L. Ostuni, A. K. S. Santha, V. S. Mattay, D. R. Weinberger, R. L. Levin, and J. A. Frank, “Analysis of interpolation effects in the reslicing of functional MR images,” J. Comput. Assist. Tomogr. 21, 803 (1997).
http://dx.doi.org/10.1097/00004728-199709000-00029
25.
25.P. Thévenaz, T. Blu, and M. Unser, Handbook of Medical Imaging Processing and Analysis (Academic Press, San Diego Ca, USA, 2000), p. 393.
26.
26.I. J. Schoenberg, “On spline functions,” Inequalities: Proc. of a Symposium (Academic Press, New York, 1967), p. 255.
27.
27.M. J. Marsden and J. Schoenberg, “On variation diminishing spline approximation methods,” Muthematica 31, 61 (1966).
28.
28.M. J. Marsden, “On uniform spline approximation,” Jl. Approximation Theory 6, 249 (1972).
http://dx.doi.org/10.1016/0021-9045(72)90056-1
29.
29.P. Thévenaz, T. Blu, and M. Unser, “Interpolation Revisited,” IEEE Trans. Med. Imaging 19, 739 (2000).
http://dx.doi.org/10.1109/42.875199
30.
30.D. Ruijters and P. Thévenaz, “GPU Prefilter for Accurate Cubic B-Spline Interpolation,” The Computer Journal 55, 15 (2012).
http://dx.doi.org/10.1093/comjnl/bxq086
31.
31.M. Unser, “Splines: a perfect fit for signal and image processing,” IEEE Signal Process. Mag. 16, 22 (1999).
http://dx.doi.org/10.1109/79.799930
32.
32.O. D. Evans and Y. Kim, “Efficient Implementation of Image Warping on a Multimedia Processor,” Real-Time Imag. 4, 417 (1998).
http://dx.doi.org/10.1006/rtim.1998.7010
33.
33.D. L. Snyder and M. I. Miller, IEEE Trans. Nucl. Sci. 32, 3864 (1985).
http://dx.doi.org/10.1109/TNS.1985.4334521
34.
34.P. P. Mondal, G. Vicidomini, and A. Diaspro, “Markov Random Field Aided Bayesian Approach for Image Reconstruction in confocal microscopy,” Jl. Appl. Phys. 102, 044701 (2007).
http://dx.doi.org/10.1063/1.2770961
35.
35.J. Besag, “Spatial Interaction and the statistical Analysis of Lattice Systems,” J. R. Stat. Soc. Ser. B (Methodol.) 36, 192 (1974).
36.
36.I. Schoenberg, Cardinal Spline Interpolation (SIAM, Philadelphia, 1973).
37.
37.I. Csiszar, “Why least squares and maximum entropy? - an axiomatic approach to inverse problems,” Ann. Stat. 19, 2032 (1991).
http://dx.doi.org/10.1214/aos/1176348385
38.
38.M. Born and E. Wolf, Principles of Optics, 6th ed. (Cambridge Univ. Press, London, 1980).
39.
39.S. W. Hell, “Far-field optical nanoscopy,” Science 316, 1153 (2007).
http://dx.doi.org/10.1126/science.1137395
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/content/aip/journal/adva/5/3/10.1063/1.4914105
2015-03-02
2016-10-01

Abstract

An iterative image reconstruction technique employing B-Spline potential function in a Bayesian framework is proposed for fluorescence microscopy images. B-splines are piecewise polynomials with smooth transition, compact support and are the shortest polynomial splines. Incorporation of the B-spline potential function in the maximum-a-posteriori reconstruction technique resulted in improved contrast, enhanced resolution and substantial background reduction. The proposed technique is validated on simulated data as well as on the images acquired from fluorescence microscopes (widefield, confocal laser scanning fluorescence and super-resolution 4Pi microscopy). A comparative study of the proposed technique with the state-of-art maximum likelihood (ML) and maximum-a-posteriori (MAP) with quadratic potential function shows its superiority over the others. B-Spline MAP technique can find applications in several imaging modalities of fluorescence microscopy like selective plane illumination microscopy, localization microscopy and STED.

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