Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
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.

Image Analysis

SCI's imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.


ross

Ross Whitaker

Segmentation
sarang

Sarang Joshi

Shape Statistics
Segmentation
Brain Atlasing
tolga

Tolga Tasdizen

Image Processing
Machine Learning
tom

Tom Fletcher

Shape Statistics
Diffusion Tensor Analysis
chris

Chris Johnson

Diffusion Tensor Analysis



Image Analysis Project Sites:


Publications in Image Analysis:


Image Reconstruction from Sensitivity Encoded MRI Data Using Extrapolated Iterations of Parallel Projections Onto Convex Sets
A.A. Samsonov, E.G. Kholmovski, C.R. Johnson. In SPIE Medical Imaging, Vol. 5032, pp. 1829--1838. 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.



Determination of the Sampling Density Compensation Function Using a Point Spread Function Modeling Approach and Gridding Approximation
A.A. Samsonov, E.G. Kholmovski, C.R. Johnson. In Proceedings of 11th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, pp. 477. 2003.



Parametric Method for Correction of Intensity Inhomogeneity in MRI Data
A.A. Samsonov, R.T. Whitaker, E.G. Kholmovski, C.R. Johnson. In Proceedings of The 10th Annual Scientific Meeting of The International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, pp. 154. 2002.



Noise-Adaptive Anisotropic Diffusion Filtering of MRI Images Reconstructed by SENSE (SENSitivity Encoding) Method
A.A. Samsonov, C.R. Johnson. In Proceedings of The 2002 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Washington DC, pp. 701--704. July, 2002.



Noise-Adaptive Nonlinear Filtering Technique for SENSE-Reconstructed Images
A.A. Samsonov, R.T. Whitaker, C.R. Johnson. In Proceedings of The 10th Annual Scientific Meeting of The International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, pp. 74. 2002.



Computational Bioimaging for Medical Diagnosis and Treatment
C.R. Johnson. In Communications of the ACM, Vol. 44, No. 3, pp. 74--76. March, 2001.



Boundary Estimation from Intensity/Color Images with Algebraic Curve Models
T. Tasdizen, D.B. Cooper. 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.



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.



Fast Isosurface Extraction Methods for Large Image Data Sets
Y. Livnat, S.G. Parker, C.R. Johnson. In Handbook of Medical Imaging, Edited by A.N. Bankman, Academic Press, San Diego, CA pp. 731--745. Nov, 2000.



An Inverse EEG Problem Solving Environment and its Applications to EEG Source Localization
D.M. Weinstein, L. Zhukov, C.R. Johnson. In NeuroImage (suppl.), pp. 921. 2000.



Interactive Source Imaging with BioPSE
D.M. Weinstein, L. Zhukov, C.R. Johnson, S.G. Parker, R. Van Uitert, R.S. MacLeod, C.D. Hansen. In Chicago 2000 World Congress on Medical Physics and Biomedical Engineering, Chicago, IL., Note: Refereed abstract., July, 2000.



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



Independent Component Analysis for EEG Source Localization in Realistic Head Models
L. Zhukov, D. Weinstein, C.R. Johnson. In IEEE Engineering in Medicine and Biology, Vol. 19, No. 3, pp. 87--96. 2000.



The BioPSE Inverse EEG Modeling Pipeline
D.M. Weinstein, P. Krysl, C.R. Johnson. In ISGG 7th International Conference on Numerical Grid Generation in Computation Field Simulations, The International Society of Grid Generation, Mississippi State University pp. 1091--1100. 2000.



Reciprocity Basis for EEG Source Imaging
L. Zhukov, D.M. Weinstein, C.R. Johnson. In NeuroImage (suppl.), pp. 598. 2000.



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



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



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



Computational and Numerical Methods for Bioelectric Field Problems
C.R. Johnson. In Critical Reviews in BioMedical Engineering, Vol. 25, No. 1, pp. 1--81. 1997.