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


 J.D. Daniels, C.T. Silva, J. Shepherd, E. Cohen. “Quadrilateral Mesh Simplification,” In ACM Transactions on Graphics - Proceedings of ACM SIGGRAPH Asia 2008, Vol. 27, No. 5, 2008.
DOI: 10.1145/1409060.1409101


S.B. Davidson, J. Freire. “Provenance and Scientific Workflows: Challenges and Opportunities,” In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1345--1350. 2008.
ISBN: 978-1-60558-102-6


C. Dietrich, J. Comba, L. Nedel, C.E. Scheidegger, C.T. Silva. “Edge Groups: A New Approach to Understanding the Mesh Quality of Marching Methods,” In IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE Visualization 2008), Vol. 14, No. 6, pp. 1651 - 1666. 2008.
DOI: 10.1109/TVCG.2008.122


G. Draper, R. Riesenfeld. “Who Votes For What? A Visual Query Language for Opinion Data,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, IEEE, pp. 1197--1204. Nov, 2008.
DOI: 10.1109/tvcg.2008.187


G. Draper, Y. Livnat, R. Riesenfeld. “A Visual Query Language for Correlation Discovery and Management,” In Proceedings of Visual and Iconic Language Conference (VaIL 2008), pp. 14--23. 2008.


T. Ellkvist, D. Koop, E.W. Anderson, J. Freire, C.T. Silva. “Using Provenance to Support Real-Time Collaborative Design of Workflows,” In Proceedings of the Second International Provenance and Annotation Workshop (IPAW 2008), pp. 266--279. 2008.


T. Ellkvist, D. Koop, J. Freire, C.T. Silva, L. Stromback. “Using Mediation to Achieve Provenance Interoperability,” In Proceedings of the 2008 Fourth IEEE International Conference on eScience, pp. 398--399. 2008.


A. Fedorov, E. Billet, M.W. Prastawa, G. Gerig, A. Radmanesh, S.K. Warfield, R. Kikinis, N. Chrisochoides. “Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures,” In Lecture Notes in Computer Science, Vol. 5358, Springer, pp. 594--603. 2008.
DOI: 10.1007/978-3-540-89639-5_57


P.T. Fletcher, S. Venkatasubramanian, S. Joshi. “Robust Statistics on Riemannian Manifolds via the Geometric Median,” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1--8. 2008.
DOI: 10.1109/CVPR.2008.4587747


N.L. Foster, A.Y. Wang, T. Tasdizen, P.T. Fletcher, J.M. Hoffman, R.A. Koeppe. “Realizing the Potential of Positron Emission Tomography with 18F-Fluorodeoxyglucose to Improve the Treatment of Alzheimer,” In Journal of the Alzheimer, Vol. 4, No. 1, Suppl. 1, pp. S29--36. 2008.
PubMed ID: 18631997


N. L. Foster, A.Y. Wang, T. Tasdizen, K. Chen, W. Jagust, R.A. Koeppe, E. Reiman, M.W. Weiner, S. Minoshima. “Cerebral Hypometabolism Suggesting Frototemporal Dementia in an Alzheimer’s Disease Clinical Trial,” In Neurology, Vol. 70, No. 11, pp. A103. 2008.


J. Freire, C.T. Silva. “Towards Enabling Social Analysis of Scientific Data,” In Proceedings of CHI Social Data Analysis Workshop 2008, 2008.

ABSTRACT

Computing has been an enormous accelerator to science and it has led to an information explosion in many different fields. Future advances in science depend on the ability to comprehend these vast amounts of data. In this paper, we discuss challenges and opportunities for social data analysis in the scientific domain.


 J. Freire, D. Koop, C.T. Silva. “Provenance for Computational Tasks: A Survey,” In Computing in Science and Engineering, Vol. 10, No. 3, pp. 11--21. 2008.


M. Fuchs, S. Gerber. “Variational Shape Detection in Microscope Images Based on Joint Shape and Image Feature Statistics,” In Proceedings of the IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2008), pp. 1--8. 2008.
DOI: 10.1109/CVPRW.2008.4563012

ABSTRACT

This paper presents a novel variational formulation incorporating statistical knowledge to detect shapes in images. We propose to train an energy based on joint shape and feature statistics inferred from training data. Variational approaches to shape detection traditionally involve energies consisting of a feature term and a regularization term. The feature term forces the detected object to be optimal with respect to image properties such as contrast, pattern or edges whereas the regularization term stabilizes the shape of the object. Our trained energy does not rely on these two separate terms, hence avoids the non-trivial task of balancing them properly. This enables us to incorporate more complex image features while still relying on a moderate number of training samples. Cell detection in microscope images illustrates the capability of the proposed method to automatically adapt itself to different image features. We also introduce a nonlinear energy and exemplarily compare it to the linear approach.


 W. Gao, Y. Chen, G. Gerig, J.K. Smith, V. Jewells, J.H. Gilmore, W. Lin. “Temporal and Spatial Development of Axonal Maturation and Myelination of White Matter in the Developing Brain,” In American Journal of Neuroradiology, pp. (published online). Nov 11, 2008.
DOI: 10.3174/ajnr.A1363


S.E. Geneser, R.M. Kirby, R.S. MacLeod. “Application of Stochastic Finite Element Methods to Study the Sensitivity of ECG Forward Modeling to Organ Conductivity,” In IEEE Transations on Biomedical Engineering, Vol. 55, No. 1, pp. 31--40. January, 2008.


J.H. Gilmore, L. Smith, H. Wolfe, B. Hertzberg, J.K. Smith, N. Chescheir, D. Evans, C. Kang, R.M. Hamer, W. Lin, G. Gerig. “Prenatal Mild Ventriculomegaly Predicts Abnormal Development of the Neonatal Brain,” In Biological Psychiatry, Vol. 64, No. 12, pp. 1069-1076. Dec, 2008.
PubMed ID: 18835482


C. Goodlett, P.T. Fletcher, J. Gilmore, G. Gerig. “Group Statistics of DTI Fiber Bundles Using Spatial Functions of Tensor Measures,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), Springer Verlag, pp. 1068--1075. 2008.
PubMed ID: 18979851


C.E. Goodyer, J. Wood, M. Berzins. “Mathematical modeling of chemical diffusion through skin using Grid-based PSEs,” In Modeling, Simulation and Optimization of Complex Processes: Proceedings of the Third International Conference on High Performance Scientific Computing, Edited by H.G. Bock and E. Kostina and H.X. Phu and R. Rannacher, Springer, pp. 249--258. 2008.


D. Gottlieb, D. Xiu. “Galerkin Method for Wave Equations with Uncertain Coefficients,” In Communications in Computational Physics, Vol. 3, No. 2, pp. 505--518. 2008.

ABSTRACT

Polynomial chaos methods (and generalized polynomial chaos methods) have been extensively applied to analyze PDEs that contain uncertainties. However this approach is rarely applied to hyperbolic systems. In this paper we analyze the properties of the resulting deterministic system of equations obtained by stochastic Galerkin projection. We consider a simple model of a scalar wave equation with random wave speed. We show that when uncertainty causes the change of characteristic directions, the resulting deterministic system of equations is a symmetric hyperbolic system with both positive and negative eigenvalues. A consistent method of imposing the boundary conditions is proposed and its convergence is established. Numerical examples are presented to support the analysis.

Keywords: Generalized polynomial chaos, stochastic PDE, Galerkin method, hyperbolic equation, uncertainty quantification