Deconvolution methods for 3-D fluorescence microscopy images

This paper presents an overview of various deconvolution techniques of 3D fluorescence microscopy images. It describes the subject of image deconvolution for 3D fluorescence microscopy images and provides an overview of the distortion issues in different areas. The paper presents a brief schematic description of fluorescence microscope systems and provides a summary of the microscope point-spread function (PSF), which often creates the most severe distortion in the acquired 3D image. Finally, it discusses the ongoing research work in the area and provides a brief review of performance measures of 3D deconvolution microscopy techniques. It also provides a summary of the numerical results using simulated data and presents the results obtained from the real data.

[1]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[2]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[3]  Hiroaki Takajo,et al.  Improved Methods of Maximum a Posteriori Restoration , 1980 .

[4]  D. Agard Optical sectioning microscopy: cellular architecture in three dimensions. , 1984, Annual review of biophysics and bioengineering.

[5]  J Bille,et al.  Reconstructing 3-D light-microscopic images by digital image processing. , 1985, Applied optics.

[6]  J. C. Dainty,et al.  Iterative blind deconvolution method and its applications , 1988 .

[7]  H T van der Voort,et al.  3-dimensional imaging of biological structures by high resolution confocal scanning laser microscopy. , 1988, Scanning microscopy.

[8]  Timothy J. Holmes Maximum-likelihood image restoration adapted for noncoherent optical imaging , 1988 .

[9]  F. S. Fay,et al.  3D Fluorescence imaging of single cells using image restoration , 1990 .

[10]  L. J. Thomas,et al.  Regularized linear method for reconstruction of three-dimensional microscopic objects from optical sections. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[11]  James R. Fienup,et al.  Joint estimation of object and aberrations by using phase diversity , 1992 .

[12]  T J Holmes,et al.  Blind deconvolution of quantum-limited incoherent imagery: maximum-likelihood approach. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[13]  T. J. Keating,et al.  Thin-section ratiometric Ca2+ images obtained by optical sectioning of fura-2 loaded mast cells , 1992, The Journal of cell biology.

[14]  Nikolas P. Galatsanos,et al.  Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation , 1992, IEEE Trans. Image Process..

[15]  S. Gibson,et al.  Experimental test of an analytical model of aberration in an oil-immersion objective lens used in three-dimensional light microscopy. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[16]  S. Joshi,et al.  Maximum a posteriori estimation with Good's roughness for three-dimensional optical-sectioning microscopy. , 1993, Journal of the Optical Society of America. A, Optics and image science.

[17]  Timothy J. Schulz,et al.  Multiframe blind deconvolution of astronomical images , 1993 .

[18]  Alberto Diaspro,et al.  3-D reconstruction in optical microscopy by a frequency-domain approach , 1993, Signal Process..

[19]  V. Mahajan Zernike circle polynomials and optical aberrations of systems with circular pupils. , 1994, Applied optics.

[20]  J N Turner,et al.  Blind deconvolution of fluorescence micrographs by maximum-likelihood estimation. , 1995, Applied optics.

[21]  H. M. Voort,et al.  Restoration of confocal images for quantitative image analysis , 1995 .

[22]  D. A. Fish,et al.  Blind deconvolution by means of the Richardson-Lucy algorithm. , 1995 .

[23]  G. M. P. VAN KEMPEN,et al.  A quantitative comparison of image restoration methods for confocal microscopy , 1997 .

[24]  Thomas M. Jovin,et al.  Acceleration of the ICTM image restoration algorithm , 1997 .

[25]  Peter J. Verveer,et al.  Efficient superresolution restoration algorithms using maximum a posteriori estimations with application to fluorescence microscopy , 1997 .

[26]  J. Conchello,et al.  Parametric blind deconvolution: a robust method for the simultaneous estimation of image and blur. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[27]  J. Conchello,et al.  Three-dimensional imaging by deconvolution microscopy. , 1999, Methods.

[28]  Whoi-Yul Kim,et al.  A region-based shape descriptor using Zernike moments , 2000, Signal Process. Image Commun..

[29]  Van Kempen,et al.  The influence of the regularization parameter and the first estimate on the performance of tikhonov regularized non-linear image restoration algorithms , 2000 .

[30]  J Boutet de Monvel,et al.  Image restoration for confocal microscopy: improving the limits of deconvolution, with application to the visualization of the mammalian hearing organ. , 2001, Biophysical journal.

[31]  P. W. Stevens,et al.  Imaging and analysis of immobilized particle arrays. , 2003, Analytical chemistry.

[32]  P. W. Stevens,et al.  Immobilized particle arrays: coalescence of planar- and suspension-array technologies. , 2003, Analytical chemistry.