Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2011, Article ID 572187, 16 pages
http://dx.doi.org/10.1155/2011/572187
Research Article

Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs

1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA

Received 2 March 2011; Revised 6 May 2011; Accepted 3 June 2011

Academic Editor: Aly A. Farag

Copyright © 2011 Linh Ha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Glaunes, A. Trouvé, and L. Younes, “Diffeomorphic matching of distributions: a new approach for unlabelled point-sets and sub-manifolds matching,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), pp. 712–718, July 2004. View at Scopus
  2. P. Lorenzen, B. Davis, and S. Joshi, “Unbiased atlas formation via large deformations metric mapping,” in the 8th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '05), vol. 3750 of Lecture Notes in Computer Science, pp. 411–418, October 2005. View at Publisher · View at Google Scholar
  3. M. I. Miller and L. Younes, “Group actions, homeomorphisms, and matching: a general framework,” International Journal of Computer Vision, vol. 41, no. 1-2, pp. 61–84, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. R. C. Knickmeyer, S. Gouttard, C. Kang et al., “A structural MRI study of human brain development from birth to 2 years,” Journal of Neuroscience, vol. 28, no. 47, pp. 12176–12182, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Datar, J. Cates, P. T. Fletcher, S. Gouttard, G. Gerig, and R. Whitaker, “Particle based shape regression of open surfaces with applications to developmental neuroimaging,” in the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '09), vol. 5762 of Lecture Notes in Computer Science, pp. 167–174, 2009. View at Publisher · View at Google Scholar
  6. H. Xue, L. Srinivasan, S. Jiang et al., “Longitudinal cortical registration for developing neonates,” in the 10th International Conference on Medical Imaging and Computer-Assisted Intervention (MICCAI '07), vol. 4792 of Lecture Notes in Computer Science, pp. 127–135, October 2007.
  7. D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast mr images,” IEEE Transactions on Medical Imaging, vol. 18, no. 8, pp. 712–721, 1999. View at Google Scholar
  8. C. E. Scheidegger, J. M. Schreiner, B. Duffy, H. Carr, and C. T. Silva, “Revisiting histograms and isosurface statistics,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, pp. 1659–1666, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. G. E. Christensen, M. I. Miller, M. W. Vannier, and U. Grenander, “Individualizing neuroanatomical atlases using a massively parallel computer,” Computer, vol. 29, no. 1, pp. 32–38, 1996. View at Google Scholar · View at Scopus
  10. A. Eklund, M. Andersson, and H. Knutsson, “Phase based volume registration using CUDA,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '10), pp. 658–661, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Micikevicius, “3D finite difference computation on GPUs using CUDA,” in the 2nd Workshop on General Purpose Processing on Graphics Processing Units (GPGPU-2 '09), p. 79, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Roberts, M. C. Sousa, and J. R. Mitchell, “A work-efficient GPU algorithm for level set segmentation,” in the 37th International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '10), July 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. C. I. Rodrigues, D. J. Hardy, J. E. Stone, K. Schulten, and W. M. W. Hwu, “GPU acceleration of cutoff pair potentials for molecular modeling applications,” in the 5th Conference on Computing Frontiers (CF '08), pp. 273–282, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Ha, J. Kruger, S. Joshi, and C. T. Silva, Multiscale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs, vol. 1, Elsevier, New York, NY, USA, 2011.
  15. B. Avants and J. C. Gee, “Geodesic estimation for large deformation anatomical shape averaging and interpolation,” NeuroImage, vol. 23, no. 1, pp. S139–S150, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Rohlfing, D. B. Russakoff, and C. R. Maurer, “Expectation maximization strategies for multi-atlas multi-label segmentation,” in the 18th International Conference on Information Processing in Medical Imaging, vol. 2732 of Lecture Notes in Computer Science, pp. 210–221, 2003.
  17. P. M. Thompson and A. W. Toga, “A framework for computational anatomy,” Computing and Visualization in Science, vol. 5, no. 1, pp. 13–34, 2002. View at Google Scholar
  18. U. Grenander and M. I. Miller, “Computational anatomy: an emerging discipline,” Quarterly of Applied Mathematics, vol. 56, no. 4, pp. 617–694, 1998. View at Google Scholar · View at Scopus
  19. F. L. Bookstein, Morphometric Tools for Landmark Data: Geometry and Biology, Cambridge University Press, 1991.
  20. M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander, “Mathematical textbook of deformable neuroanatomies,” Proceedings of the National Academy of Sciences of the United States of America, vol. 90, no. 24, pp. 11944–11948, 1993. View at Publisher · View at Google Scholar · View at Scopus
  21. G. E. Christensen, R. D. Rabbitt, and M. I. Miller, “Deformable templates using large deformation kinematics,” IEEE Transactions on Image Processing, vol. 5, no. 10, pp. 1435–1447, 1996. View at Google Scholar · View at Scopus
  22. M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes, “Computing large deformation metric mappings via geodesic flows of diffeomorphisms,” International Journal of Computer Vision, vol. 61, no. 2, pp. 139–157, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. V. Camion and L. Younes, “Geodesic interpolating splines,” in Proceedings of the 3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR '01), pp. 513–527, London, UK, 2001.
  24. S. C. Joshi and M. I. Miller, “Landmark matching via large deformation diffeomorphisms,” IEEE Transactions on Image Processing, vol. 9, no. 8, pp. 1357–1370, 2000. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Vaillant and J. Glaunès, “Surface matching via currents,” in the 19th International Conference on Information Processing in Medical Imaging (IPMI '05), pp. 381–392, July 2005. View at Scopus
  26. J. Glaunès, A. Qiu, M. I. Miller, and L. Younes, “Large deformation diffeomorphic metric curve mapping,” International Journal of Computer Vision, vol. 80, no. 3, pp. 317–336, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. A. W. Toga and P. Thompson, “Brain warping,” in Brain Warping, pp. 1–26, Academic Press, New York, NY, USA, 1999. View at Google Scholar
  28. J. A. Glaunes and S. Joshi, “Template estimation from unlabeled point set data and surfaces for computational anatomy,” in the International Workshop on Mathematical Foundations of Computational Anatomy, 2006.
  29. B. C. Davis, E. Bullitt, P. T. Fletcher, and S. Joshi, “Population shape regression from random design data,” in the 11th IEEE International Conference on Computer Vision (ICCV '07), October 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Durrleman, X. Pennec, A. Trouvé, P. Thompson, and N. Ayache, “Inferring brain variability from diffeomorphic deformations of currents: an integrative approach,” Medical Image Analysis, vol. 12, no. 5, pp. 626–637, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “Automated model-based tissue classification of MR images of the brain,” IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 897–908, 1999. View at Google Scholar · View at Scopus
  32. S. K. Warfield, M. Kaus, F. A. Jolesz, and R. Kikinis, “Adaptive, template moderated, spatially varying statistical classification,” Medical Image Analysis, vol. 4, no. 1, pp. 43–55, 2000. View at Google Scholar · View at Scopus
  33. M. Prastawa, J. H. Gilmore, W. Lin, and G. Gerig, “Automatic segmentation of MR images of the developing newborn brain,” Medical Image Analysis, vol. 9, no. 5, pp. 457–466, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. W. E. Lorensen and H. E. Cline, “Marching cubes: a high resolution 3D surface construction algorithm,” ACM SIGGRAPH Computer Graphics, vol. 21, no. 4, pp. 163–169, 1987. View at Google Scholar · View at Scopus
  35. Z. Zhang, “Iterative point matching for registration of free-form curves and surfaces,” International Journal of Computer Vision, vol. 13, no. 2, pp. 119–152, 1994. View at Publisher · View at Google Scholar · View at Scopus
  36. H. A. El Munim and A. A. Farag, “Shape representation and registration using vector distance functions,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), June 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. K. M. Pohl, J. Fisher, S. Bouix et al., “Using the logarithm of odds to define a vector space on probabilistic atlases,” Medical Image Analysis, vol. 11, no. 5, pp. 465–477, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Tsai, A. Yezzi, W. Wells et al., “A shape-based approach to the segmentation of medical imagery using level sets,” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 137–154, 2003. View at Publisher · View at Google Scholar · View at Scopus
  39. J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, and J. C. Phillips, “GPU computing,” Proceedings of the IEEE, vol. 96, no. 5, Article ID 4490127, pp. 879–899, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. M. Harris, J. Owens, S. Sengupta, Y. Zhang, and A. Davidson, CUDPP: CUDA Data Parallel Primitives Library, 2007.
  41. D. G. Merrill and A. S. Grimshaw, “Revisiting sorting for GPGPU stream architectures,” in the 19th International Conference on Parallel Architectures and Compilation Techniques (PACT '10), pp. 545–546, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. S. Durrleman, X. Pennec, A. Trouvé, and N. Ayache, “Sparse approximation of currents for statistics on curves and surfaces,” in the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '08), vol. 5242 of Lecture Notes in Computer Science, pp. 390–398, September 2008. View at Publisher · View at Google Scholar
  43. L. Greengard and J. Strain, “The fast gauss transform,” SIAM Journal on Scientific and Statistical Computing, vol. 12, no. 1, pp. 79–94, 1991. View at Google Scholar
  44. R. W. Hockney and J. W. Eastwood, Computer Simulation Using Particles, Taylor and Francis, 1989.
  45. A. Trouvé and L. Younes, “Metamorphoses through lie group action,” Foundations of Computational Mathematics, vol. 5, no. 2, pp. 173–198, 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. P. Aljabar, K. K. Bhatia, M. Murgasova et al., “Assessment of brain growth in early childhood using deformation-based morphometry,” NeuroImage, vol. 39, no. 1, pp. 348–358, 2008. View at Publisher · View at Google Scholar · View at Scopus