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


 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.


E. Jurrus, R.T. Whitaker, B. Jones, R. Marc, T. Tasdizen. “An Optimal-Path Approach for Neural Circuit Reconstruction,” In Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1609--1612. 2008.
PubMed ID: 19172170


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


T. Tasdizen. “Principal Components for Non-Local Means of Image Denoising,” In Proceedings of the International Conference on Image Processing (ICIP 2008), pp. 1728--1731. 2008.
PubMed ID: 19180227


T. Tasdizen , E. Jurrus, R. T. Whitaker. “Non-uniform Illumination Correction in Transmission Electron Microscopy,” In MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, 2008.

ABSTRACT

Transmission electron microscopy (TEM) provides resolutions on the order of a nanometer. Hence, it is a critical imaging modality for biomedical analysis at the sub-cellular level. One of the problems associated with TEM images is variations in brightness due to electron imaging defects or non-uniform support films and specimen staining. These variations render image processing operations such as segmentation more difficult. The correction requires estimation of the global illumination field. In this paper, we propose an automatic method for estimating the illumination field using only image intensity gradients. The closed-form solution is very fast to compute.

2007


 G. Adluru, S.P. Awate, T. Tasdizen, R.T. Whitaker, E.V.R. DiBella. “Temporally Constrained Reconstruction of Dynamic Cardiac Perfusion MRI,” In Magnetic Resonance in Medicine, Vol. 57, pp. 1027--1036. 2007.


S. Gerber, T. Tasdizen, R.T. Whitaker. “Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmaps,” In Proceedings of the 2007 International Conference on Machine Learning (ICML), pp. 281--288. 2007.


B.W. Jones, R.E. Marc, C.B. Watt, K. Kinardi, D. DeMill, J.H. Yang, T. Tasdizen, P. Koshevoy, E. Jurrus, R.T. Whitaker. “Structure and Function of Microneuromas in Retinal Remodeling,” In The Association for Research in Vision and Ophthalmology (ARVO) Conference, Note: (abstract), 2007.


P.A. Koshevoy, T. Tasdizen, R.T. Whitaker. “Automatic Assembly of TEM Mosaics and Mosaic Stacks Using Phase Correlation,” SCI Institute Technical Report, No. UUSCI-2007-004, University of Utah, 2007.


O. Nemitz, T. Tasdizen, M. Rumpf, R.T. Whitaker. “Anisotropic Curvature Motion for Structure Enhancing Smoothing of 3D MR Angiography Data,” In Journal of Mathematical Imaging and Vision, Vol. 7, No. 3, pp. 217--229. 2007.
DOI: 10.1007/s10851-006-0645-2


N. Sadeghi, T. Tasdizen, N.L. Foster, A.Y. Wang, S. Minoshima, A.P. Lieberman.. “Automatic Classification of Alzheimers Disease vs. Frontotemporal Dementia: A Decision Tree Approach with FDG-PET,” SCI Institute Technical Report, No. UUSCI-2007-016, University of Utah, 2007.

2006


 S.P. Awate, T. Tasdizen, N. Foster, R.T. Whitaker. “Adaptive, Nonparametric Markov Modeling for Unsupervised, MRI Brain-Tissue Classification,” SCI Institute Technical Report, No. UUSCI-2006-008, University of Utah, 2006.


S.P. Awate, T. Tasdizen, R.T. Whitaker. “Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics,” In Proceedings of The European Conference on Computer Vision (ECCV) 2006 Springer, Lecture Notes in Computer Science, pp. 494--507. 2006.


S.P. Awate, T. Tasdizen, R.T. Whitaker. “Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics,” SCI Institute Technical Report, No. UUSCI-2006-011, University of Utah, 2006.


S.P. Awate, E.V.R. DiBella, T. Tasdizen, R.T. Whitaker. “Model-Based Image Reconstruction for Dynamic Cardiac Perfusion MRI from Sparse Data,” In Proceedings of the 2006 IEEE Int. Conf. Engineering and Biology Society (EMBC), pp. 936--941. 2006.


S.P. Awate, T. Tasdizen, N.L. Foster, R.T. Whitaker. “Adaptive, Nonparametric Markov Modeling for Unsupervised, MRI Brain-Tissue Classification,” In Medical Image Analysis (MEDIA), Vol. 10, No. 5, pp. 726--739. 2006.


E. Jurrus, T. Tasdizen, P. Koshevoy, P.T. Fletcher, M. Hardy, C-B. Chien, W. Denk, R.T. Whitaker. “Axon Tracking in Serial Block-Face Scanning Electron Microscopy,” In Workshop on Microscopic Image Analysis with Applications in Biology, MICCAI, October, 2006.


P.A. Koshevoy, T. Tasdizen, R.T. Whitaker. “Implementation of an Automatic Slice-to-Slice Registration Tool,” SCI Institute Technical Report, No. UUSCI-2006-018, University of Utah, 2006.


P.A. Koshevoy, T. Tasdizen, R.T. Whitaker. “Implementation of an Automatic Image Registration Tool,” SCI Institute Technical Report, No. UUSCI-2006-020, University of Utah, 2006.