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

2008


 H. Nguyen, E.Y. Kang, J. Freire. “Automatically Extracting Form Labels,” In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, pp. 1498--1500. 2008.


T. Ochotta, D. Saupe. “Image-Based Surface Compression,” In Computer Graphics Forum, Vol. 27, No. 6, pp. 1647--1663. 2008.


I. Oguz, J. Cates, P.T. Fletcher, R.T. Whitaker, D. Cool, S. Aylward, M. Styner. “Cortical Correspondence using Entropy-Based Particle Systems and Local Features,” In 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008. (ISBI 2008), pp. 1637--1640. 2008.


R. Palmer, M. DeLisi, G. Gopalakrishnan, R.M. Kirby. “An Approach to Formalization and Analysis of Message Passing Libraries,” In Proceedings of the 12th International Workshop on Formal Methods for Industrial Critical Systems (FMICS 2007), Berlin, Germany, Vol. 4916/2008, Note: Awarded Best Paper., pp. 164--181. 2008.


R.P. Pawlowski, J.P. Simonis, H.F. Walker, J.N. Shadid. “Inexact Newton Dogleg Methods,” In SIAM Journal on Numerical Analysis, Vol. 46, No. 4, pp. 2112--2132. 2008.
DOI: 10.1137/050632166

ABSTRACT

The dogleg method is a classical trust-region technique for globalizing Newton's method. While it is widely used in optimization, including large-scale optimization via truncated-Newton approaches, its implementation in general inexact Newton methods for systems of nonlinear equations can be problematic. In this paper, we first outline a very general dogleg method suitable for the general inexact Newton context and provide a global convergence analysis for it. We then discuss certain issues that may arise with the standard dogleg implementational strategy and propose modified strategies that address them. Newton–Krylov methods have provided important motivation for this work, and we conclude with a report on numerical experiments involving a Newton–GMRES dogleg method applied to benchmark CFD problems.


 V. Pegoraro, I. Wald, S.G. Parker. “Sequential Monte Carlo Adaptation in Low-Anisotropy Participating Media,” In Proceedings of the 19th Eurographics Symposium on Rendering, pp. 1097--1104. 2008.


V. Pegoraro, C. Brownlee, P.S. Shirley, S.G. Parker. “Towards Interactive Global Illumination Effects via Sequential Monte Carlo Adaptation,” In Proceedings of the 3rd IEEE Symposium on Interactive Ray Tracing, pp. 107--114. 2008.


N.S. Phatak, S.A. Maas, A.I. Veress, N.A. Pack, E.V.R. Di Bella, J.A. Weiss. “Strain measurement in the left ventricle during systole with deformable image registration,” In Medical Image Analysis, pp. (in press). 2008.


S. Pizer, M. Styner, T. Terriberry, R. Broadhurst, S. Joshi, E. Chaney, P.T. Fletcher. “Statistical Applications with Deformable M-Reps,” In Computational Imaging and Vision, Springer, pp. 269--308. 2008.
DOI: 10.1007/978-1-4020-8658-8_9


K. Potter, J. Krüger, C.R. Johnson. “Towards the Visualization of Multi-Dimentional Stochastic Distribution Data,” In Proceedings of The International Conference on Computer Graphics and Visualization (IADIS) 2008, pp. 191--196. 2008.


M.W. Prastawa, G. Gerig. “Brain Lesion Segmentation Through Physical Model Estimation,” In Lecture Notes in Computer Science, Vol. 5358, pp. 562--571. 2008.
DOI: 10.1007/978-3-540-89639-5_54


T. Preusser, H. Scharr, K. Krajsek, R.M. Kirby. “Building Blocks for Computer Vision with Stochastic Partial Differential Equations,” In International Journal of Computer Vision, Vol. 80, No. 3, Springer, pp. 375--405. July, 2008.
DOI: 10.1007/s11263-008-0145-5


J.C. Principe, J. Xu, R. Jenssen, A.R.C. Paiva, I. Park. “A Reproducing Kernel Hilbert Space framework for Information-Theoretic Learning,” In IEEE Transactions on Signal Processing, Vol. 56, No. 12, Edited by J.C. Principe, IEEE, pp. 5891--5902. Dec, 2008.
ISBN: 978-1-4419-1569-6
DOI: 10.1109/tsp.2008.2005085


M. Rullmann, A. Anwander, M. Dannhauer, S.K. Warfield, F.H. Duffy, C.H. Wolters. “EEG Source Analysis of Epileptiform Activity Using a 1mm Anisotropic Hexahedra Finite Element Head Model.,” In NeuroImage, Vol. 44, No. 2, pp. 399--410. 2008.
DOI: 10.1016/j.neuroimage.2008.09.009


N. Sadeghi, N.L. Foster, A.Y. Wang, S. Minoshima, A.P. Lieberman, T. Tasdizen. “Automatic Classification of Alzheimer,” In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI 2008): From Nano to Macro, pp. 408--411. 2008.
DOI: 10.1109/ISBI.2008.4541019


E. Santos, L. Lins, J.P. Ahrens, J. Freire, C.T. Silva. “A First Study on Clustering Collections of Workflow Graphs,” In Proceedings of the Second International Provenance and Annotation Workshop (IPAW 2008), pp. 160--173. 2008.


C.E. Scheidegger, D. Koop, E. Santos, H.T. Vo, S.P. Callahan, J. Freire, C.T. Silva.. “Tackling the Provenance Challenge One Layer at a Time,” In Concurrency Computat.: Pract. Exper., Vol. 20, No. 5, pp. 473--483. 2008.


C.E. Scheidegger, H.T. Vo, D. Koop, J. Freire, C.T. Silva. “Querying and Re-Using Workflows with VisTrails,” In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1251--1254. 2008.
DOI: 10.1145/1376616.1376747


J.A. Schmidt. “Uintah Application Development,” SCI Technical Report, No. UUSCI-2008-005, University of Utah, 2008.


M. Schott, V. Pegoraro, C.D. Hansen, K. Boulanger, J. Stratton, K. Bouatouch. “A Directional Occlusion Shading Model for Interactive Direct Volume Rendering,” SCI Technical Report, No. UUSCI-2008-009, University of Utah Scientific Computing and Imaging Institute, 2008.