The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2012 vol.18)
pp: 2130-2139
Jian Chen , University of Maryland Baltimore County
Haipeng Cai , University of Southern Mississippi
Alexander P. Auchus , University of Mississippi Medical Center
David H. Laidlaw , Brown University
We report the impact of display characteristics (stereo and size) on task performance in diffusion magnetic resonance imaging (DMRI) in a user study with 12 participants. The hypotheses were that (1) adding stereo and increasing display size would improve task accuracy and reduce completion time, and (2) the greater the complexity of a spatial task, the greater the benefits of an improved display. Thus we expected to see greater performance gains when detailed visual reasoning was required. Participants used dense streamtube visualizations to perform five representative tasks: (1) determine the higher average fractional anisotropy (FA) values between two regions, (2) find the endpoints of fiber tracts, (3) name a bundle, (4) mark a brain lesion, and (5) judge if tracts belong to the same bundle. Contrary to our hypotheses, we found the task completion time was not improved by the use of the larger display and that performance accuracy was hurt rather than helped by the introduction of stereo in our study with dense DMRI data. Bigger was not always better. Thus cautious should be taken when selecting displays for scientific visualization applications. We explored the results further using the body-scale unit and subjective size and stereo experiences.
Data visualization, Retina, Virtual environments, Lesions, Stereo image processing, Magnetic resonance imaging, virtual environment, Display characteristics, diffusion tensor MRI
Jian Chen, Haipeng Cai, Alexander P. Auchus, David H. Laidlaw, "Effects of Stereo and Screen Size on the Legibility of Three-Dimensional Streamtube Visualization", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2130-2139, Dec. 2012, doi:10.1109/TVCG.2012.216
[1] A. Bair, D. House, and C. Ware, Texturing of layered surfaces for optimal viewing IEEE Transactions on Visualization and Computer Graphics, 12(5): 1125-1132. 2006.
[2] R. Ball, C. North, and D. Bowman., Move to improve: promoting physical navigation to increase user performance with large displays. In Proc. of the SIGCHI conference on Human factors in computing systems, pages 191-200. 2007.
[3] M. Bar, Visual objects in context Nature Reviews Neuroscience, 5(8): 617-629, 2004.
[4] P. J. Basser, S. Pajevic, C. Pierpaoli., J. Duda, and A. Aldroubi., In vivo fiber tractography using DT-MRI data Magnetic Resonance in Medicine, 44: 625-632, 2000.
[5] J. Bertin., Semiology of graphics: diagrams, networks, maps. University of Wisconsin Press, 1983.
[6] E. Boring., Sensation and perception in the history of experimental psychology. Appleton-Century-Crofts Inc., 1942.
[7] D. Bowman and R. McMahan., Virtual reality: how much immersion is enough? Computer, 40(7): 36-43, 2007.
[8] J. Chen, A. Auchus, and D. Laidlaw. DMRI display study, .
[9] S. Correia, S. Lee, T. Voorn., D. Tate, R. Paul., S. Zhang, S. Salloway., P. Malloy, and D. Laidlaw, Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI Neuroimage, 42(2): 568-581, 2008.
[10] M. Czerwinski, D. Tan, and G. Robertson., Women take a wider view. In Proc. of the SIGCHI conference on Human factors in computing systems, pages 195-202, 2002.
[11] C. Demiralp, C. Jackson, D. Karelitz., S. Zhang, and D. Laidlaw, Cave and fishtank virtual-reality displays: A qualitative and quantitative com-parison IEEE Transactions on Visualization and Computer Graphics, 12(3): 323-330, 2006.
[12] S. Diepenbrock, J. Prassni, F. Lindemann., H. Bothe, and T. Ropinski, 2010 IEEE visualization contest winner: Interactive planning for brain tumor resections IEEE Computer Graphics and Applications, 31: 6-13, 2011.
[13] N. Elmqvist and P. Tsigas, A taxonomy of 3D occlusion management for visualization IEEE Transactions on Visualization and Computer Graphics, 14(5): 1095-1109, 2008.
[14] M. Everts, H. Bekker, J. Roerdink,, and T. Isenberg., Depth-dependent halos: Illustrative rendering of dense line data. IEEE Transactions on Visualization and Computer Graphics, 15(6): 1299-1306, 2009.
[15] A. Forsberg, J. Chen, and D. Laidlaw, Comparing 3D vector field visualization methods: A user study IEEE Transactions on Visualization and Computer Graphics, 15(6): 1219-1226, 2009.
[16] M. Hlawatsch, J. Vollrath, F. Sadlo,, and D. Weiskopf., Coherent structures of characteristic curves in symmetric second order tensor fields. IEEE Transactions on Visualization and Computer Graphics, 17(6): 781-794, 2011.
[17] J. Hollerbach, W. Thompson, and P. Shirley, The convergence of robotics, vision, and computer graphics for user interaction The International Journal of Robotics Research, 18(11): 1088-1100, 1999.
[18] V. Interrante, Perceiving and representing shape and depth PhD thesis, UNC-Chapel Hill, Department of Computer Science, 1996.
[19] V. Interrante and C. Grosch, Visualizing 3D flow IEEE Computer Graphics and Applications, 18(4): 49-53, 1998.
[20] B. Jellison, A. Field, J. Medow., M. Lazar, M. Salamat,, and A. Alexan-der., Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. American Journal of Neuroradiology, 25(3): 356, 2004.
[21] D. Keirn., Designing pixel-oriented visualization techniques: Theory and applications. IEEE Transactions on Visualization and Computer Graphics, 6(1): 59-78, 2000.
[22] G. Kindlmann, R. Whitaker, T. Tasdizen,, and T. Moller., Curvature-based transfer functions for direct volume rendering: Methods and applications. In IEEE Visualization, pages 513-520, 2003.
[23] R. Kosara, C. Healey, V. Interrante., D. Laidlaw, and C. Ware., User studies: why, how, and when? IEEE Computer Graphics and Applications, 23(4): 20-25, 2003.
[24] B. Laha, K. Sensharma, J. Schiffbauer,, and D. Bowman., Effects of immersion on visual analysis of volume data. IEEE Transactions on Visualization and Computer Graphics, 18: 597-606, 2012.
[25] J. LaViola, A. Forsberg, D. Laidlaw,, and A. van Dam., Virtual reality-based interactive scientific visualization environments. Trends in Interactive Visualization, pages 225-250, 2009.
[26] D. Mizell, S. Jones, M. Slater,, and B. Spanlang., Comparing immersive virtual reality with other display modes for visualizing complex 3D ge-ometry. University College London, technical report, 2002.
[27] T. Munzner, A nested model for visualization design and validation IEEE Transactions on Visualization and Computer Graphics, 15(6): 921-928, 2009.
[28] T. Ni, D. Bowman, and J. Chen., Increased display size and resolution improve task performance in information-rich virtual environments. In Proc. of Graphics Interface, pages 139-146, 2006.
[29] R. Pausch, D. Proffitt, and G. Williams., Quantifying immersion in virtual reality. In Proc. of the 24th annual conference on computer graphics and interactive techniques, pages 13-18, 1997.
[30] R. Peikert, H. Hauser, H. Carr,, and R. Fuchs., Topological Methods in Data Analysis and Visualization II: Theory, Algorithms, and Applications. Springer Verlag, 2012.
[31] D. Penney, J. Chen, and D. Laidlaw., Effects of illumination, texture, and motion on task performance in streamtube visualization of diffusion tensor MRI. pages 97-104, 2012.
[32] Prabhat M. Katzourin, K. Wharton,, and M. Slater., A comparative study of desktop, fishtank, and cave systems for the exploration of volume rendered confocal data sets. IEEE Transactions on Visualization and Computer Graphics, 14: 551-563, 2008.
[33] W. Qi, R. Taylor, C. Healey,, and J. Martens., A comparison of immersive HMD, fish tank VR and fish tank with haptics displays for volume visualization. In Proc. of the 3rd Symposium on Applied Perception in Graphics and Visualization, pages 51-58, 2006.
[34] P. Rheingans and D. Ebert, Volume illustration: Nonphotorealistic rendering of volume models IEEE Transactions on Visualization and Computer Graphics, 7(3): 253-264, 2001.
[35] J. Roeckelein., Dictionary of theories, laws, and concepts in psychology. Greenwood Pub Group, 1998.
[36] R. Rosenholtz, Y. Li, and L. Nakano, Measuring visual clutter Journal of Vision, 7(2): 1-22, 2007.
[37] R. Ruddle, S. Payne, and D. Jones., Navigating large-scale virtual envi-ronments: what differences occur between helmet-mounted and desk-top displays? Presence: Teleoperators & Virtual Environments, 8(2): 157-168, 1999.
[38] T. Schultz., Feature extraction for DW-MRI visualization: The state of the art and beyond. In Proc. of Schloss Dagstuhl Scientific Visualization Workshop, 2010.
[39] T. Schultz., Topological features in 2D symmetric higher-order tensor fields. 30(3): 841-850, 2011.
[40] B. Shneiderman., The eyes have it: A task by data type taxonomy for information visualizations. In Proc. of IEEE Symposium on Visual Lan-guage, pages 336-343, 1996.
[41] J. Swan, J. Gabbard, D. Hix., R. Schulman, K. Kim,et al. A comparative study of user performance in a map-based virtual environment. In Proc. of IEEE Virtual Reality, pages 259-266, 2003.
[42] D. Tan, D. Gergle, P. Scupelli,, and R. Pausch., Physically large displays improve performance on spatial tasks. ACM Transactions on Computer-Human Interaction (TOCH]), 13(1): 71-99, 2006.
[43] A. Vilanova, G. Berenschot, and C. van Pul, DTI visualization with streamsurfaces and evenly-spaced volume seeding. In Proc. of the Eu-rographics Symposium on Visualization, pages 173-182, 2004.
[44] C. Ware., Dynamic stereo displays. In Proc. of the SIGCHI conference on Humanfactors in computing systems, pages 310-316, 1995.
[45] C. Ware., Information Visualization: Perception for Design. Morgan Kaufmann Publishers, second edition, 2004.
[46] C. Ware and G. Franck, Evaluating stereo and motion cues for visualizing information nets in three dimensions ACM Transactions on Graphics, 15(2): 121-140, 1996.
[47] B. Yost, Y. Haciahmetoglu, and C. North., Beyond visual acuity: the perceptual scalability of information visualizations for large displays. In Proc. of the SIGCHI conference on Humanfactors in computing systems, pages 101-110, 2007.
[48] C. Zanbaka, B. Lok, S. Babu., A. Ulinski, and L. Hodges, Comparison of path visualizations and cognitive measures relative to travel technique in a virtual environment IEEE Transactions on Visualization and Computer Graphics. 11(6): 694-705, 2005.
[49] S. Zhang, C. Demiralp, and D. Laidlaw, Visualizing diffusion tensor MR images using streamtubes and streamsurfaces IEEE Transactions on Visualization and Computer Graphics, 9(4): 454-462, 2003.
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool