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Organ Shape Modeling Based on the Laplacian Deformation Framework for Surface-Based Morphometry Studies

  • Kim, Jae-Il (Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Park, Jin-Ah (Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST))
  • Received : 2012.08.11
  • Accepted : 2012.09.03
  • Published : 2012.09.30

Abstract

Recently, shape analysis of human organs has achieved much attention, owing to its potential to localize structural abnormalities. For a group-wise shape analysis, it is important to accurately restore the shape of a target structure in each subject and to build the inter-subject shape correspondences. To accomplish this, we propose a shape modeling method based on the Laplacian deformation framework. We deform a template model of a target structure in the segmented images while restoring subject-specific shape features by using Laplacian surface representation. In order to build the inter-subject shape correspondences, we implemented the progressive weighting scheme for adaptively controlling the rigidity parameter of the deformable model. This weighting scheme helps to preserve the relative distance between each point in the template model as much as possible during model deformation. This area-preserving deformation allows each point of the template model to be located at an anatomically consistent position in the target structure. Another advantage of our method is its application to human organs of non-spherical topology. We present the experiments for evaluating the robustness of shape modeling against large variations in shape and size with the synthetic sets of the second cervical vertebrae (C2), which has a complex shape with holes.

Keywords

References

  1. G. B. Frisoni, R. Ganzola, E. Canu, U. Rub, F. B. Pizzini, F. Alessandrini, G. Zoccatelli, A. Beltramello, C. Caltagirone, and P. M. Thompson, "Mapping local hippocampal changes in Alzheimer's disease and normal ageing with MRI at 3 Tesla," Brain, vol. 131, no. part 12, pp. 3266-3276, 2008. https://doi.org/10.1093/brain/awn280
  2. B. Patenaude, S. M. Smith, D. N. Kennedy, and M. Jenkinson, "A Bayesian model of shape and appearance for subcortical brain segmentation," Neuroimage, vol. 56, no. 3, pp. 907-922, 2011. https://doi.org/10.1016/j.neuroimage.2011.02.046
  3. A. El-Bazl, M. Nitzken, F. Khalifa, A. Elnakib, G. Gimel'farb, R. Falk, and M. A. El-Ghar, "3D shape analysis for early diagnosis of malignant lung nodules," Information Processing in Medical Imaging, Lecture Notes in Computer Science vol. 6801, G. Szekely and H. K. Hahn, editors, Heidelberg: Springer Berlin, pp. 772-783, 2011.
  4. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models-their training and application," Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, 1995. https://doi.org/10.1006/cviu.1995.1004
  5. T. Heimann and H. P. Meinzer, "Statistical shape models for 3D medical image segmentation: a review," Medical Image Analysis, vol. 13, no. 4, pp. 543-563, 2009. https://doi.org/10.1016/j.media.2009.05.004
  6. A. F. Frangi, W. J. Niessen, D. Rueckert, and J. A. Schnabel, "Automatic 3D ASM construction via atlas-based landmarking and volumetric elastic registration," Information Processing in Medical Imaging, Lecture Notes in Computer Science vol. 2082, M. F. Insana and R. M. Leahy, editors, Heidelberg: Springer Berlin, pp. 78-91, 2001.
  7. A. Souza and J. K. Udupa, "Automatic landmark selection for active shape models," Proceedings of the SPIE, vol. 5747, pp. 1377-1383, 2005.
  8. S. Rueda, J. K. Udupa, and L. Bai, "Shape modeling via local curvature scale," Pattern Recognition Letters, vol. 31, no. 4, pp. 324-336, 2010. https://doi.org/10.1016/j.patrec.2009.09.007
  9. M. Styner, I. Oguz, S. Xu, C. Brechbuhler, D. Pantazis, J. J. Levitt, M. E. Shenton, and G. Gerig, "Framework for the statistical shape analysis of brain structures using SPHARMPDM," Insight Journal, no. 1071, pp. 242-250, 2006.
  10. H. Kim, T. Mansi, A. Bernasconi, and N. Bernasconi, "Vertex- wise shape analysis of the hippocampus: disentangling positional differences from volume changes," Proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, Toronto, Canada, 2011, pp. 352-359.
  11. M. Alexa, "Differential coordinates for local mesh morphing and deformation," The Visual Computer, vol. 19, no. 2-3, pp. 105-114, 2003.
  12. O. Sorkine, "Differential representations for mesh processing," Computer Graphics Forum, vol. 25, no. 4, pp.789-807, 2006 https://doi.org/10.1111/j.1467-8659.2006.00999.x
  13. A. Guimond, J. Meunier, J. P. Thirion, "Average brain models: a convergence study," Computer Vision and Image Understanding, vol. 77, no. 2, pp. 192-210, 2000. https://doi.org/10.1006/cviu.1999.0815
  14. G. Heitz, T. Rohlfing, and C. R. Maurer Jr, "Statistical shape model generation using nonrigid deformation of a template mesh," Proceedings of SPIE, vol. 5747, pp. 1411-1421, 2005.
  15. G. Turk, "Re-tiling polygonal surfaces," ACM SIGGRAPH Computer Graphics, vol. 26 no. 2, pp. 55-64, 1992.
  16. A. H. Nasri, T. Kim, and K. Lee, "Polygonal mesh regularization for subdivision surfaces interpolating meshes of curves," The Visual Computer, vol. 19, no. 2-3, pp. 80-93, 2003.

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