SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

An internationally recognized leader in visualization, scientific computing, and image analysis

SCI Publications

2010


M. Waldner, W. Puff, A. Lex, M. Streit, D. Schmalstieg. “Visual Links across Applications,” In Proceedings of the Conference on Graphics Interface (GI '10), Canadian Human-Computer Communications Society, pp. 129--136. 2010.
ISBN: 1568817125

ABSTRACT

The tasks carried out by modern information workers become increasingly complex and time-consuming. They often require to evaluate, interpret, and compare information from different sources presented in multiple application windows. With large, high resolution displays, multiple application windows can be arranged in a way so that a large amount of information is visible simultaneously. However, individual application windows' contents and visual representations are isolated and relations between information items contained in these windows are not explicit. Thus, relating and comparing information across applications has to be executed manually by the user, which is a tedious and error-prone task.

In this paper we present visual links connecting related pieces of information across application windows and thereby guiding the user's attention to relevant information. Applications are coordinated by a management application accessible via a light-weight interface. User selections are synchronized across registered applications and visual links are rendered on top of the desktop content by a window manager. Initial user feedback was very positive and indicates that visual links improve task efficiency when analyzing information from multiple sources.


D.F. Wang, R.M. Kirby, C.R. Johnson. “Resolution Strategies for the Finite-Element-Based Solution of the ECG Inverse Problem,” In IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp. 220--237. February, 2010.


Y. Wan, C.D. Hansen. “Fast Volumetric Data Exploration with Importance-Based Accumulated Transparency Modulation,” In Proceedings of IEEE/EG International Symposium on Volume Graphics 2010, pp. 61--68. 2010.
DOI: 10.2312/VG/VG10/061-068

ABSTRACT

Direct volume rendering techniques have been successfully applied to visualizing volumetric datasets across many application domains. Due to the sensitivity of transfer functions and the complexity of fine-tuning transfer functions, direct volume rendering is still not widely used in practice. For fast volumetric data exploration, we propose Importance-Based Accumulated Transparency Modulation which does not rely on transfer function manipulation. This novel rendering algorithm is a generalization and extension of the Maximum Intensity Difference Accumulation technique. By only modifying the accumulated transparency, the resulted volume renderings are essentially high dynamic range. We show that by using several common importance measures, different features of the volumetric datasets can be highlighted. The results can be easily extended to a high-dimensional importance difference space, by mixing the results from an arbitrary number of importance measures with weighting factors, which all control the final output with a monotonic behavior. With Importance-Based Accumulated Transparency Modulation, the end-user can explore a wide variety of volumetric datasets quickly without the burden of manually setting and adjusting a transfer function.


D.F. Wang, R.M. Kirby, R.S. MacLeod, C.R. Johnson. “A New Family of Variational-Form-Based Regularizers for Reconstructing Epicardial Potentials from Body-Surface Mapping,” In Computing in Cardiology, 2010, pp. 93--96. 2010.


C.H. Wolters, S. Lew, R.S. MacLeod, M.S. Hämäläinen. “Combined EEG/MEG source analysis using calibrated finite element head models,” In Proc. of the 44th Annual Meeting, DGBMT, Note: to appear, http://conference.vde.com/bmt-2010, Rostock-Warnemünde, Germany, Oct.5-8, 2010 2010.


H. Zhang, S.P. Awate, S.R. Das, J.H. Woo, E.R. Melhem, J.C. Gee, P.A. Yushkevich. “A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features,” In Medical Image Analysis, Vol. 14, No. 5, pp. 666--673. 2010.
DOI: 10.1016/j.media.2010.05.002

2009


J.R. Anderson, B.W. Jones, J.-H. Yang, M.V. Shaw, C.B. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U.V., R.T. Whitaker, D. Mastronarde, T. Tasdizen, R.E. Marc. “A Computational Framework for Ultrastructural Mapping of Neural Circuitry,” In PLoS Biology, Vol. 7, No. 3, pp. e74. 2009.
PubMed ID: 19855814


J. Anderson, B. Jones, J. Yang, M. Shaw, C. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U.V., R.T. Whitaker, D. Mastronarde, T. Tasdizen, R. Marc. “Ultra Structural Mapping of Neural Circuitry: A Computational Framework,” In IEEE International Symposium on Biomedical Engineering (ISBI 2009), pp. 1135--1137. 2009.
DOI: 10.1109/ISBI.2009.5193257

ABSTRACT

Complete mapping of neuronal networks requires data acquisition at synaptic resolution with canonical coverage of tissues and robust neuronal classification. Transmission electron microscopy (TEM) remains the optimal tool for network mapping. However, capturing high resolution, large, serial section TEM (ssTEM) image volumes is complicated by the need to precisely mosaic distorted image tiles and subsequently register distorted mosaics. Moreover, most cell or tissue class markers are not optimized for TEM imaging. We present a complete framework for neuronal reconstruction at ultrastructural resolution, allowing the elucidation of complete neuronal circuits. This workflow combines TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration and terabyte-scale image browsing for volume annotation. Networks that previously would require decades of assembly can now be completed in months, enabling large-scale connectivity analyses of both new and legacy data. Additionally, these approaches can be extended to other tissue or biological network systems.

Keywords: crcns, neural circuitry


R. Archibald, A. Gelb, R. Saxena, D. Xiu. “Discontinuity Detection in Multivariate Space for Stochastic Simulations,” In Journal of Computational Physics, Vol. 228, No. 7, pp. 2676--2689. 2009.
DOI: 10.1016/j.jcp.2009.01.001

ABSTRACT

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Edge detection has traditionally been associated with detecting physical space jump discontinuities in one dimension, e.g. seismic signals, and two dimensions, e.g. digital images. Hence most of the research on edge detection algorithms is restricted to these contexts. High dimension edge detection can be of significant importance, however. For instance, stochastic variants of classical differential equations not only have variables in space/time dimensions, but additional dimensions are often introduced to the problem by the nature of the random inputs. The stochastic solutions to such problems sometimes contain discontinuities in the corresponding random space and a prior knowledge of jump locations can be very helpful in increasing the accuracy of the final solution. Traditional edge detection methods typically require uniform grid point distribution. They also often involve the computation of gradients and/or Laplacians, which can become very complicated to compute as the number of dimensions increases. The polynomial annihilation edge detection method, on the other hand, is more flexible in terms of its geometric specifications and is furthermore relatively easy to apply. This paper discusses the numerical implementation of the polynomial annihilation edge detection method to high dimensional functions that arise when solving stochastic partial differential equations.

Keywords: Stochastic partial differential equations, Multivariate edge detection, Generalized polynomial chaos method


T.J. Badger, R.S. Oakes, M. Daccarett, N.S. Burgon, N. Akoum, E.N. Fish, J.J. Blauer, S.N. Rao, Y. Adjei-Poku, E.G. Kholmovski, S. Vijayakumar, E.V. Di Bella, R.S. MacLeod, N.F. Marrouche. “Temporal Left Atrial Lesion Formation After Ablation of Atrial Fibrillation,” In Heart Rhythm, Vol. 6, No. 2, pp. 161--168. February, 2009.


T.J. Badger, Y.A. Adjei-Poku, N.S. Burgon, S. Kalvaitis, A. Shaaban, D.N. Sommers, J.J.E. Blauer, E.N. Fish N. Akoum, T.S. Haslem, E.G. Kholmovski, R.S. MacLeod, D.G. Adler, N.F. Marrouche. “Initial Experience of Assessing Esophageal Tissue Injury and Recovery Using Delayed-Enhancement MRI After Atrial Fibrillation Ablation,” In Circulation: Arrhythmia and Electrophysiology, Vol. 2, pp. 620--625. 2009.


M. Berzins. “Data Bounded Polynomials and Preserving Positivity in High Order ENO and WENO Methods,” SCI Technical Report, No. UUSCI-2009-003, SCI Institute, University of Utah, 2009.


E.W. Bethel, C.R. Johnson, S. Ahern, J. Bell, P.-T. Bremer, H. Childs, E. Cormier-Michel, M. Day, E. Deines, P.T. Fogal, C. Garth, C.G.R. Geddes, H. Hagen, B. Hamann, C.D. Hansen, J. Jacobsen, K.I. Joy, J. Krüger, J. Meredith, P. Messmer, G. Ostrouchov, V. Pascucci, K. Potter, Prabhat, D. Pugmire, O. Rubel, A.R. Sanderson, C.T. Silva, D. Ushizima, G.H. Weber, B. Whitlock, K. Wu. “Occam's Razor and Petascale Visual Data Analysis,” In Journal of Physics: Conference Series, Journal of Physics: Conference Series, Vol. 180, No. 012084, pp. (published online). 2009.
DOI: 10.1088/1742-6596/180/1/012084

ABSTRACT

One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today's largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena.


J. Brouillat, C. Bouville, B. Loos, C.D. Hansen, K. Bouatouch. “A Bayesian Monte Carlo Approach to Global Illumination,” In Computer Graphics Forum Journal, Early View, Vol. 28, No. 8, pp. 2315--2329. October, 2009.


M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. “A Meshing Pipeline for Biomedical Models,” In Engineering with Computers, Vol. 25, No. 1, SpringerLink, pp. 115-130. 2009.
DOI: 10.1007/s00366-008-0106-1


A.N.M. Imroz Choudhury, S.G. Parker. “Ray Tracing NPR-Style Feature Lines,” In Proceedings of the 7th International Symposium on Non-Photorealistic Animation and Rendering (NPAR) 2009, pp. 5--14. 2009.


J.D. Daniels, C.T. Silva, E. Cohen. “Semi-Regular Quadrilateral-only Remeshing from Simplified Base Domains,” In Computer Graphics Forum, Computer Graphics Forum, Vol. 28, No. 5, Wiley-Blackwell, pp. 1427--1435. July, 2009.
DOI: 10.1111/j.1467-8659.2009.01519.x


J.D. Daniels, C.T. Silva, E. Cohen. “Localized Quadrilateral Coarsening,” In Computer Graphics Forum, Vol. 28, No. 5, Wiley-Blackwell, pp. 1437--1444. July, 2009.
DOI: 10.1111/j.1467-8659.2009.01520.x


M. Datar, J. Cates, P.T. Fletcher, S. Gouttard, G. Gerig, R.T. Whitaker. “Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5762, pp. 167--174. 2009.
DOI: 10.1007/978-3-642-04271-3_21
PubMed ID: 20426109


C. Deitrich, C.E. Scheidegger, J. Schreiner, J. Comba, L.P. Nedel, C.T. Silva. “Edge Transformations for Improving Mesh Quality of Marching Cubes,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 15, No. 1, pp. 150--159. Sept/Oct, 2009.