SCI Publications
2009
S. Durrleman, X. Pennec, A. Trouvé, G. Gerig, N. Ayache.
“Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5761, pp. 297--304. 2009.
DOI: 10.1007/978-3-642-04268-3_37
PubMed ID: 20426000
T. Ellkvist, L. Stromback, L. Lins, J. Freire.
“A First Study on Strategies for Generating Workflow Snippets,” In Proceedings of the ACM SIGMOD Intenational Workshop on Keyword Search on Structured Data (KEYS), pp. 15--20. 2009.
ISBN: 978-1-60558-570-3
T. Ellkvist, D. Koop, J. Freire, C.T. Silva, L. Stromback.
“Using Mediation to Achieve Provenance Interoperability,” In Proceedings of the IEEE International Workshop on Scientific Workflows, 2009, pp. 291--298. 2009.
ISBN: 978-0-7695-3708-5
M. Ellisman, R. Stevens, M. Colvin, T. Schlick, E. Delong, G. Olsen, J. George, G. Karniakadis, C.R. Johnson, N. Sematova.
“Scientific Grand Challenges: Opportunities in biology at the Extreme Scale of Computing,” Note: DOE Office of Advanced Scientific Computing Research, August, 2009.
T. Etiene, C.E. Scheidegger, L.G. Nonato, R. Kirby, C.T. Silva.
“Verifiable Visualization for Isosurface Extraction,” In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2009 IEEE Visualization Conference, Vol. 15, No. 6, pp. 1227--1234. Sept/Oct, 2009.
P.T. Fletcher PT, S. Venkatasubramanian, S. Joshi.
“The geometric median on Riemannian manifolds with application to robust atlas estimation,” In Neuroimage, Vol. 45, No. 1, pp. S143--S152. March, 2009.
PubMed ID: 19056498
P.T. Fletcher, J. Moeller, J.M. Phillips, S. Venkatasubramanian.
“Computing Hulls In Positive Definite Space,” In In Proceedings of the 19th Fall Workshop on Computational Geometry, November, 2009.
T. Fogal, J. Krüger.
“Size Matters - Revealing Small Scale Structures in Large Datasets,” In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, IFMBE Proceedings, Vol. 25/13, Springer Berlin Heidelberg, pp. 41--44. 2009.
J. Freire.
“Provenance Management: Challenges and Opportunities,” In Datenbanksysteme in Business, Technologie und Web (BTW), pp. 4. 2009.
W. Gao, W. Lin, Y. Chen, G. Gerig, J.K. Smith, V. Jewells, J.H. Gilmore.
“Temporal and Spatial Development of Axonal Maturation and Myelination of White Matter in the Developing Brain,” In American Journal of Neuroradiology (AJNR), Vol. 30, pp. 290--296. 2009.
PubMed ID: 19001533
S.E. Geneser, R.M. Kirby, Brian Wang, B. Salter, S. Joshi.
“Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapy,” In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 688--700. 2009.
PubMed ID: 19694304
S. Gerber, T. Tasdizen, S. Joshi, R.T. Whitaker.
“On the Manifold Structure of the Space of Brain Images,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Springer, pp. 305--312. 2009.
DOI: 10.1007/978-3-642-04268-3_38
PubMed ID: 20426001
S. Gerber, T. Tasdizen, R.T. Whitaker.
“Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds,” In Proceedings of the 2009 International Conference on Computer Vison (ICCV 2009), pp. 529--536. September, 2009.
ISSN: 1550-5499
DOI: 10.1109/ICCV.2009.5459193
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represented as a parametrized surface represented by a set of parameters that are defined on the input samples. The representation also provides a natural mapping from high to low dimensional space, and a concatenation of these two mappings induces a projection operator onto the manifold. The explicit projection operator allows for a clearly defined objective function in terms of projection distance and reconstruction error. A formulation of the mappings in terms of kernel regression permits a direct optimization of the objective function and the extremal points converge to principal surfaces as the number of data to learn from increases. Principal surfaces have the desirable property that they, informally speaking, pass through the middle of a distribution. We provide a proof on the convergence to principal surfaces and illustrate the effectiveness of the proposed approach on synthetic and real data sets.
C. Goodlett, P.T. Fletcher, J.H. Gilmore, G. Gerig.
“Group analysis of DTI fiber tract statistics with application to neurodevelopment,” In Neuroimage, Vol. 45, No. 1 (suppl 1), pp. S133--S142. 2009.
PubMed ID: 19059345
C. Goodlett, P.T. Fletcher, J.H. Gilmore, G. Gerig.
“Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment,” In NeuroImage, Vol. 45, pp. S133--S142. December, 2009.
DOI: 10.1016/j.neuroimage.2008.10.060
PubMed ID: 19059345
PubMed Central ID: PMC2727755
Diffusion tensor imaging (DTI) provides a unique source of information about the underlying tissue structure of brain white matter in vivo including both the geometry of major fiber bundles as well as quantitative information about tissue properties represented by derived tensor measures. This paper presents a method for statistical comparison of fiber bundle diffusion properties between populations of diffusion tensor images. Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. Diffusion properties, such as fractional anisotropy (FA) and tensor norm, along fiber tracts are modeled as multivariate functions of arc length. Hypothesis testing is performed non-parametrically using permutation testing based on the Hotelling T(2) statistic. The linear discriminant embedded in the T(2) metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of 1 and 2 year old subjects, a significant increase in FA and a correlated decrease in Frobenius norm was found in several tracts. Significant differences in neonates were found in the splenium tract between controls and subjects with isolated mild ventriculomegaly (MVM) demonstrating the potential of this method for clinical studies.
S. Gouttard, M.W. Prastawa, E. Bullitt, W. Lin, C. Goodlett, G. Gerig.
“Constrained Data Decomposition and Regression for Analyzing Healthy Aging from Fiber Tract Diffusion Properties,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5761, pp. 321--328. 2009.
PubMed ID: 20426003
J. Guilkey, T. Harman, J. Luitjens, J. Schmidt, J. Thornock, J.D. de St. Germain, S. Shankar, J. Peterson, C. Brownlee.
“Uintah User Guide Version 1.1,” SCI Technical Report, No. UUSCI-2009-007, SCI Institute, University of Utah, 2009.
L.K. Ha, J. Krüger, T. Fletcher, S. Joshi, C.T. Silva.
“Fast Parallel Unbiased Diffeomorphic Atlas Construction on Multi-Graphics Processing Units,” In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization 2009, 2009.
DOI: 0.2312/EGPGV/EGPGV09/041-048
Unbiased diffeomorphic atlas construction has proven to be a powerful technique for medical image analysis, particularly in brain imaging. The method operates on a large set of images, mapping them all into a common coordinate system, and creating an unbiased common template for studying intra-population variability and interpopulation differences. The technique has also proven effective in tissue and object segmentation via registration of anatomical labels. However, a major barrier to the use of this approach is its high computational cost. Especially with the increasing number of inputs and data size, it becomes impractical even with a fully optimized implementation on CPUs. Fortunately, the highly element-wise independence of the problem makes it well suited for parallel processing. This paper presents an efficient implementation of unbiased diffeomorphic atlas construction on the new parallel processing architecture based on Multi-Graphics Processing Units (Multi-GPUs). Our results show that the GPU implementation gives a substantial performance gain on the order of twenty to sixty times faster than a single CPU and provides an inexpensive alternative to large distributed-memory CPU clusters.
L.K. Ha, J. Krüger, C.T. Silva.
“Fast 4-way parallel radix sorting on GPUs,” In Computer Graphic Forum, 2009.
Efficient sorting is a key requirement for many computer science algorithms. Acceleration of existing techniques as well as developing new sorting approaches is crucial for many realtime graphics scenarios, database systems, and numerical simulations to name just a few. It is one of the most fundamental operations to organize and filter the ever growing massive amounts of data gathered on a daily basis. While optimal sorting models for serial execution on a single processor exist, efficient parallel sorting remains a challenge. In this paper we present a hardware-optimized parallel implementation of the radix sort algorithm that results in a significant speed up over existing sorting implementations. We outperform all known GPU based sorting systems by about a factor of two and eliminate restrictions on the sorting key space. This makes our algorithm not only the fastest, but also the first general GPU sorting solution.
C.D. Hansen, C.R. Johnson, V. Pascucci, C.T. Silva.
“Visualization for Data-Intensive Science,” In The Fourth Paradigm: Data-Intensive Science, Edited by S. Tansley and T. Hey and K. Tolle, Microsoft Research, pp. 153--164. 2009.