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Issue No.06 - November/December (2009 vol.15)
pp: 1489-1496
Yong Wan , Scientific and Imaging Institute at University of Utah
Hideo Otsuna , Department of Neurobiology and Anatomy at University of Utah
Chi-Bin Chien , Department of Neurobiology and Anatomy at University of Utah
Charles Hansen , Scientific and Imaging Institute at University of Utah
ABSTRACT
Confocal microscopy is widely used in neurobiology for studying the three-dimensional structure of the nervous system. Confocal image data are often multi-channel, with each channel resulting from a different fluorescent dye or fluorescent protein; one channel may have dense data, while another has sparse; and there are often structures at several spatial scales: subneuronal domains, neurons, and large groups of neurons (brain regions). Even qualitative analysis can therefore require visualization using techniques and parameters fine-tuned to a particular dataset. Despite the plethora of volume rendering techniques that have been available for many years, the techniques standardly used in neurobiological research are somewhat rudimentary, such as looking at image slices or maximal intensity projections. Thus there is a real demand from neurobiologists, and biologists in general, for a flexible visualization tool that allows interactive visualization of multi-channel confocal data, with rapid fine-tuning of parameters to reveal the three-dimensional relationships of structures of interest. Together with neurobiologists, we have designed such a tool, choosing visualization methods to suit the characteristics of confocal data and a typical biologist's workflow. We use interactive volume rendering with intuitive settings for multidimensional transfer functions, multiple render modes and multi-views for multi-channel volume data, and embedding of polygon data into volume data for rendering and editing. As an example, we apply this tool to visualize confocal microscopy datasets of the developing zebrafish visual system.
INDEX TERMS
Visualization, neurobiology, confocal microscopy, qualitative analysis, volume rendering
CITATION
Yong Wan, Hideo Otsuna, Chi-Bin Chien, Charles Hansen, "An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research", IEEE Transactions on Visualization & Computer Graphics, vol.15, no. 6, pp. 1489-1496, November/December 2009, doi:10.1109/TVCG.2009.118
REFERENCES
[1] H. Aizawa, I. Bianco, T. Hamaoka, T. Miyashita, O. Uemura, M. Concha, C. Russell, S. Wilson, and H. Okamoto, Laterotopic representation of left-right information onto the dorso-ventral axis of a zebrafish midbrain target nucleus. Current Biology, 15: 238–243, 2005.
[2] Bitplane AG, Imaris, 2009. http://www.bitplane.com/go/ products/imaris.
[3] H. H. Blthoff and H. A. Mallot, Interaction of different modules in depth perception. IEEE/IAPR First Intl. Conf on Computer Vision, pages 295–305, 1987.
[4] W. Cai and G. Sakas, Data intermixing and multi-volume rendering. Computer Graphics Forum, 18 (3): 359–368, 1999.
[5] C. Everitt, Interactive Order-Independent Transparency. White paper, Nvidia, 1999. http://developer.nvidia.com/object/ Interactive_Order_Transparency.html.
[6] T. J. Fellers and M. W. Davidson, Introduction to Confocal Microscopy, 2008. http://www.olympusconfocal.com/ theory/confocalintro.html.
[7] T. J. Fellers, K. M. Vogt, and M. W. Davidson, CCD Signal-To-Noise Ratio, 2008. http://www.microscopyu.com/tutorials/ java/digitalimaging/signaltonoise/index.html.
[8] S. Grimm, Real-Time Mono- and Multi-Volume Rendering of Large Medical Datasets on Standard PC Hardware. PhD thesis, Vienna University of Technology, Gaullachergasse 33/35, 1160 Vienna, Austria, February 2005.
[9] Improvision. Volocity, High performance 3D imaging software, 2008. http://www.improvision.com/products/volocity/ visualization/.
[10] F. Janoos, B. Nouansengsy, X. Xu, R. Machiraju, and S. T. Wong, Classification and uncertainty visualization of dendritic spines from optical microscopy imaging. Computer Graphics Forum, 27 (3): 879–886, may 2008.
[11] J. Kniss, G. Kindlmann, and C. Hansen, Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics, 8 (3): 270–285, 2002.
[12] K. Kreeger and A. Kaufman, Mixing translucent polygons with volumes. In Proceedings of IEEE Visualization 1999, pages 191–198, 1999.
[13] K. Mosaliganti, L. Cooper, R. Sharp, R. Machiraju, G. Leone, K. Huang, and J. Saltz, Reconstruction of cellular biological structures from optical microscopy data. IEEE Transactions on Visualization and Computer Graphics, 14 (4): 863–876, 2008.
[14] Z. Nagy and R. Klein, Depth-peeling for texture-based volume rendering. Proceedings of the 11th Pacific Conference on Computer Graphics and Applications, pages 429–433, 2003.
[15] H. Otsuna and K. Ito, Systematic analysis of the visual projection neurons of drosophila melanogaster. i. lobula-specific pathways. Journal of Comparative Neurology, 497 (6): 928–958, 2006.
[16] J. B. Pawley, Handbook of Biological Confocal Microscopy, 2nd edition. Springer, 1995.
[17] D. W. Piston, G. H. Patterson, J. Lippincott-Schwartz, N. S. Claxton, and M. W. Davidson, Introduction to Fluorescent Proteins, 2008. http://www.microscopyu.com/articles/ livecellimaging/fpintro.html.
[18] C. Rezk-Salama, K. Engel, M. Bauer, G. Greiner, and T. Ertl, Interactive volume rendering on standard pc graphics hardware using multi-textures and multi-stage rasterization. In Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware, pages 109–118, New York, NY, USA, 2000. ACM.
[19] C. Rezk-Salama, M. Keller, and P. Kohlmann, High-level user interfaces for transfer function design with semantics. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 1021–1028, 2006.
[20] F. Rossler, E. Tejada, T. Fangmeier, T. Ertl, and M. Knauff, Gpu-based multi-volume rendering for the visualization of functional brain images. In Proceedings of SimVis 2006, pages 305–318, 2006.
[21] T. Sato, T. Hamaoka, H. Aizawa, T. Hosoya, and H. Okamoto, Genetic single-cell mosaic analysis implicates ephrinb2 reverse signaling in projections from the posterior tectum to the hindbrain in zebrafish. Journal of Neuroscience, 27 (20): 5271–5279, 2007.
[22] SCI Institute, University of Utah. Seg3D, 2008. http://software. sci.utah.edu/SCIRunDocs/index.php/CIBC:Seg3D.
[23] T. J. T and M. E., Perception of surface curvature and direction of illumination from patterns of shading. Journal of Experimental Psychology: Human Perception and Performance, 9 (4): 583–595, 1983.
[24] A. VanGelder and K. Kim, Direct volume rendering with shading via three-dimensional textures. In 1996 Volume Visualization Symposium, pages 23–30. IEEE, 1996.
[25] Visage Imaging. Amira, 2008. http://www.amiravis.com/overview.html.
[26] D. Weiskopf, K. Engel, and T. Ertl, Interactive clipping techniques for texture-based volume visualization and volume shading. IEEE Transactions on Visualization and Computer Graphics, 9 (3): 298–312, 2003.
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