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Wavelet Compression of Three-Dimensional Time-Lapse Biological Image Data

Published online by Cambridge University Press:  28 January 2005

H. Narfi Stefansson ,
Kevin W. Eliceiri ,
Charles F. Thomas ,
Amos Ron ,
Ron DeVore ,
Robert Sharpley  and
John G. White
H. Narfi Stefansson
Affiliation:
Department of Mathematics and Computer Science, University of Wisconsin, Madison, WI 53706, USA
Kevin W. Eliceiri
Affiliation:
Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA
Charles F. Thomas
Affiliation:
Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA
Amos Ron
Affiliation:
Department of Mathematics and Computer Science, University of Wisconsin, Madison, WI 53706, USA
Ron DeVore
Affiliation:
Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA
Robert Sharpley
Affiliation:
Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA
John G. White
Affiliation:
Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA
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Abstract

The use of multifocal-plane, time-lapse recordings of living specimens has allowed investigators to visualize dynamic events both within ensembles of cells and individual cells. Recordings of such four-dimensional (4D) data from digital optical sectioning microscopy produce very large data sets. We describe a wavelet-based data compression algorithm that capitalizes on the inherent redunancies within multidimensional data to achieve higher compression levels than can be obtained from single images. The algorithm will permit remote users to roam through large 4D data sets using communication channels of modest bandwidth at high speed. This will allow animation to be used as a powerful aid to visualizing dynamic changes in three-dimensional structures.

Keywords

wavelet image data compressiondigital microscopy
Type
BIOLOGICAL APPLICATIONS
Information
Microscopy and Microanalysis , Volume 11 , Issue 1 , February 2005 , pp. 9 - 17
Copyright
© 2005 Microscopy Society of America

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References

REFERENCES

Boutet de Monvel, J., Le Calvez, S., & Ulfendahl, M. (2001). Image restoration for confocal microscopy: Improving the limits of deconvolution, with application to the visualization of the mammalian hearing organ. Biophys J 80, 2455–2470.Google Scholar
Brislawn, C.M. (1996). Classification of nonexpansive symmetric extension transforms for multirate filter banks. Applied and Computational Haromonic Analysis 3, 337–357.Google Scholar
Centonze, V.E. & White, J.G. (1998). Multiphoton excitation provides optical sections from deeper within scattering specimens than confocal imaging. Biophys J 75, 2015–2024.Google Scholar
Chalfie, M., Tu, Y., Euskirchen, G., Ward, W.W., & Prasher, D.C. (1994). Green fluorescent protein as a marker for gene expression. Science 263, 802–805.Google Scholar
Christopoulos, C.A., Skodras, A.N., & Ebrahimi, T. (2000). The JPEG2000 still image coding system: An overview. IEEE Trans Consumer Electronics 46, 1103–1127.Google Scholar
Daubechies, I. (1992). Ten Lectures on Wavelets. CBMS Regional Conference Series in Applied Mathematics. Philadelphia, PA: SIAM.
Denk, W., Strickler, J.H., & Webb, W.W. (1990). Two-photon laser scanning fluorescence microscopy. Science 248, 73–76.Google Scholar
Devore, R., Jawerth, B., & Lucier, B. (1992). Image compression through transform coding. IEEE Proc Info Theory 38, 719–746.Google Scholar
Eliceiri, K.W., Rueden, C., Mohler, W.A., Hibbard, W.L., & White, J.G. (2002). Analysis of multidimensional biological image data. Biotechniques 33, 1268–1273.Google Scholar
Gao, Z., Andreev, A., & Sharpley, R.C. (1997). Data compression and elementary encoding of wavelet coefficients. Technical Report, Columbia, SC: The University of South Carolina Math Department.
Kamath, R.S. & Ahringer, J. (2003). Genome-wide RNAi screening in Caenorhabditis elegans. Methods 30, 313–321.Google Scholar
Nomarski, G. (1955). Microinterferometre differentiel a ondes polarisees. J Phys Radium 16, 9–13.Google Scholar
Skodras, A.N., Christopoulos, C.A., & Ebrahimi, T. (2001). JPEG2000: The upcoming still image compression standard. Patt Recog Lett 22, 1337–1345.Google Scholar
Squirrell, J.M., Wokosin, D.L., White, J.G., & Bavister, B.D. (1999). Long-term two-photon fluorescence imaging of mammalian embryos without compromising viability. Nature Biotechnol 17, 763–767.Google Scholar
Strang, G. & Nguyen, B. (1996). Wavelets and Filter Banks. Wellesley, MA: Wellesley-Cambridge Press.
Thomas, C., DeVries, P., Hardin, J., & White, J. (1996). Four-dimensional imaging: Computer visualization of 3D movements in living specimens. Science 273, 603–607.Google Scholar