Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Visualization

Visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, biomedical data analysis, healthcare data analysis, weather data analysis, poetry, network and graph analysis, financial data analysis, etc.

Research involves novel algorithm and technique development to building tools and systems that assist in the comprehension of massive amounts of (scientific) data. We also research the process of creating successful visualizations.

We strongly believe in the role of interactivity in visual data analysis. Therefore, much of our research is concerned with creating visualizations that are intuitive to interact with and also render at interactive rates.

Visualization at SCI includes the academic subfields of Scientific Visualization, Information Visualization and Visual Analytics.


chuck

Charles Hansen

Volume Rendering
Ray Tracing
Graphics
pascucci

Valerio Pascucci

Topological Methods
Data Streaming
Big Data
chris

Chris Johnson

Scalar, Vector, and
Tensor Field Visualization,
Uncertainty Visualization
mike

Mike Kirby

Uncertainty Visualization
ross

Ross Whitaker

Topological Methods
Uncertainty Visualization
miriah

Miriah Meyer

Information Visualization
yarden

Yarden Livnat

Information Visualization
alex lex

Alex Lex

Information Visualization
bei

Bei Wang

Information Visualization
Scientific Visualization
Topological Data Analysis
 

Visualization Project Sites:


Associated Labs:


Publications in Visualization:


Cramer-Rao Bounds for Nonparametric Surface Reconstruction from Range Data
T. Tasdizen, R.T. Whitaker. In Proceedings of Fourth International Conference on 3-D Imaging and Modeling, pp. 70--77. October, 2003.



Particle-Based Simulation of Fluids
S. Premoze, T. Tasdizen, J. Bigler, A.E. Lefohn, R. T. Whitaker. In Eurographics, pp. 401--410. 2003.



Feature preserving variational smoothing of terrain data
T. Tasdizen, R.T. Whitaker. In IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision, October, 2003.



Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications
G. Kindlmann, R.T. Whitaker, T. Tasdizen, T. Möller. In Proceedings Visualization 2003, pp. 67. October, 2003.



BioPSE Case Study: Modeling, Simulation, and Visualization of Three Dimensional Mouse Heart Propagation
D.M. Weinstein, J.V. Tranquillo, C.S. Henriquez, C.R. Johnson. In International Journal of Bioelectromagnetism, Vol. 5, No. 1, pp. 314--315. 2003.



Geometric Surface Processing via Normal Maps
T. Tasdizen, R.T. Whitaker, P. Burchard, S. Osher. In ACM Transactions on Graphics, 2003.



A Next Step: Visualizing Errors and Uncertainty
C.R. Johnson, A.R. Sanderson. In IEEE Computer Graphics and Applications, Vol. 23, No. 5, pp. 6--10. September/October, 2003.



A Note on Dynamic Data Driven Application Simulation (DDDAS) Using Virtual Telemetry
C.C. Douglas, C.E. Shannon, Y. Efendiev, R.E. Ewing, V. Ginting, R. Lazanov, M.J. Cole, G.M. Jones, C.R. Johnson, J. Simpson. In International Conference on Parallel Algorithms and Computing Environments, pp. pp.193--198. 2003.



Geometric Surface Smoothing via Anisotropic Diffusion of Normals
T. Tasdizen, R.T. Whitaker, P. Burchard, S. Osher. In Proceeding of IEEE Visualization 2002, pp. 125--132. 2002.



The transfer function bake-off
H. Pfister, B. Lorensen, C. Bajaj, G. Kindlmann, W. Schroeder, L.S. Avila, K.M. Raghu, R. Machiraju, J. Lee. In IEEE Computer Graphics and Applications, Vol. 21, No. 1, IEEE, pp. 16--22. 2001.
DOI: 10.1109/38.920623



Modeling and Simulation in Medicine: Towards an Integrated Framework
G. Higgins, B. Athey, J. Bassingthwaighte, J. Burgess, H. Champion, K. Cleary, P. Dev, J. Duncan, M. Hopmeier, D. Jenkins, C.R. Johnson, H. Kelly, R. Leitch, W. Lorensen, D. Metaxas, V. Spitzer, N. Vaidehi, K. Vosburgh, R. Winslow. In Computer Aided Surgery, Vol. 6, No. 1, Note: Final report of the meeting of the same title held July 20-21, 2000, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA., 2001.
DOI: 10.1002/igs.1008



Computational Engineering and Science Program at the University of Utah
C. DeTar, A.L. Fogelson, C.R. Johnson, C.A. Sikorski. In Proceedings of the International Conference on Computational Science (ICCS) 2001, San Francisco, Edited by V. Alexandrov and J. Dongarra and B. Juliano and R. Renner and K. Tan, pp. 1176--1185. May, 2001.



A Prototype System For Synergistic Data Display
J.D. Brederson, M. Ikits, C.R. Johnson, C.D. Hansen. In IEEE Virtual Reality 2001, 2001.



Computational Field Visualization
C.R. Johnson, D. Brederson, C.D. Hansen, M. Ikits, G. Kindlmann, Y. Livnat, S.G. Parker, D.M. Weinstein, R.T. Whitaker. In Computer Graphics, Vol. 35, No. 4, pp. 5--9. 2001.



Isosurface extraction for large-scale datasets
Y. Livnat, C.D. Hansen, S.G. Parker, C.R. Johnson. In Proceedings of Scientific Visualization -Dagstuhl`2000, Edited by F. Post, 2001.



Quantitative Comparative Evaluation of 2D Vector Field Visualization Methods
D.H. Laidlaw, R.M. Kirby, J.S. Davidson, T.S. Miller, M. da Silva, W.H. Warren, M. Tarr. In Proceedings of IEEE Visualization 2001, San Diego, CA, pp. 143--150. October, 2001.



Topology Preserving Smoothing of Vector Fields
R. Westermann, C.R. Johnson, T. Ertl. In IEEE Trans. Vis & Comp. Graph., Vol. 7, No. 3, pp. 222--229. 2001.
DOI: 10.1109/2945.942690

Proposes a technique for topology-preserving smoothing of sampled vector fields. The vector field data is first converted into a scalar representation in which time surfaces implicitly exist as level sets. We then locally analyze the dynamic behavior of the level sets by placing geometric primitives in the scalar field and by subsequently distorting these primitives with respect to local variations in this field. From the distorted primitives, we calculate the curvature normal and we use the normal magnitude and its direction to separate distinct flow features. Geometrical and topological considerations are then combined to successively smooth dense flow fields, at the same time retaining their topological structure.

Keywords: vector field methods, ip image processing signal processing, surface processing, ncrr



Dynamic Data Driven Application Systems: Creating a dynamic and symbiotic coupling of application/simulations with measurements/experiments
A. Deshmukh, C.C. Douglas, M. Ball, R.E. Ewing, C.R. Johnson, C. Kesselman, C. Lee. Note: 28 pages, Edited by W. Powell, R. Sharpley, National Science Foundation, 2000.



Statistical Analysis For FEM EEG Source Localization in Realistic Head Models
School of Computing Technical Report, L. Zhukov, D. Weinstein, C.R. Johnson. No. UUCS-2000-003, University of Utah, February, 2000.



The Visual Haptic Workbench
J.D. Brederson, M. Ikits, C.R. Johnson, C.D. Hansen, J.M. Hollerbach. In Proceedings of the Fifth PHANToM Users Group Workshop, pp. 46--49. October, 2000.