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.

SCI Publications


T. Tasdizen, D.M. Weinstein, J.N. Lee. “Automatic Tissue Classification for the Human Head from Multispectral MRI,” SCI Institute Technical Report, No. UUSCI-2004-001, University of Utah, March, 2004.

T. Tasdizen, R.T. Whitaker. “Higher-order nonlinear priors for surface reconstruction,” In IEEE Trans. Pattern Anal. & Mach. Intel., Vol. 26, No. 7, pp. 878--891. July, 2004.

T. Tasdizen, R.T. Whitaker. “An Efficient, Geometric Multigrid Solver for the Anisotropic Diffusion Equation in Two and Three Dimensions,” SCI Institute Technical Report, No. UUSCI-2004-002, University of Utah, June, 2004.


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

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

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

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

T. Tasdizen, R.T. Whitaker. “Anisotropic diffusion of surface normals for feature preserving surface reconstruction,” In Proceedings of Fourth International Conferenceon 3-D Imaging and Modeling, pp. 353--360. October, 2003.

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


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


T. Tasdizen, J.-P. Tarel, D.B. Cooper. “Improving the Stability of Algebraic Curves for Applications,” In IEEE Transactions on Image Processing, Vol. 9, No. 3, pp. 405--416. March, 2000.

T. Tasdizen, D.B. Cooper. “Boundary Estimation from Intensity/Color Images with Algebraic Curve Models,” In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, IEEE, 2000.
DOI: 10.1109/icpr.2000.905308


A concept and algorithm are presented for non-iterative robust estimation of piecewise smooth curves of maximal edge strength in small image windows-typically 8/spl times/8 to 32/spl times/32. This boundary-estimation algorithm has the nice properties that it uses all the data in the window and thus can find locally weak boundaries embedded in noise or texture and boundaries when there are more than two regions to be segmented in a window; it does not require step edges-but handles ramp edges well. The curve-estimates found are among the level sets of a dth degree polynomial fit to "suitable" weightings of the image gradient vector at each pixel in the image window. Since the polynomial fitting is linear least squares, the computation to this point is very fast. Level sets then chosen to be appropriate boundary curves are those having the highest differences in average gray level in regions to either side. This computation is also fast. The boundary curves and segmented regions found are suitable for all purposes but especially for indexing using algebraic curve invariants in this form.


T. Tasdizen, J.-P. Tarel, D. B. Cooper. “Algebraic curves that work better,” In Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 1999.
DOI: 10.1109/cvpr.1999.784605


T. Tasdizen, L. Akarun, C. Ersoy. “Color Quantization with Genetic Algorithms,” In Signal Processing: Image Communication, Vol. 12, pp. 49--57. 1998.


Z. Lei, T. Tasdizen, D.B. Cooper. “PIMS and Invariant Parts for Shape Recognition,” In Sixth International Conference on Computer Vision, Narosa Publishing House, 1997.
DOI: 10.1109/iccv.1998.710813