Abstract
Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU ).
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References
Bachtar F, Chen X, Hisada T (2006) Finite element contact analysis of the hip joint. Medical and Biological Engineering and Computing 44(8):643–651
Boyd SK, Müller R (2006) Smooth surface meshing for automated finite element model generation from 3D image data. Journal of Biomechanics 39(7):1287–1295
Carreras IA, Sorzano C, Marabini R, Carazo J, De Solorzano CO, Kybic J (2006) Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization. Computer Vision Approaches to Medical Image Analysis, pp. 85–95
Geman D (1990) Random Fields and Inverse Problems in Imaging. In: Saint-Flour Lectures 1988, Lecture Notes in Mathematics, pp. 113–193
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6): 721–741
Göddeke D (2005) GPGPU Basic Math Tutorial. Technical report, FB Mathematik, Universität Dortmund
Green S (2005) The OpenGL Framebuffer Object Extension. NVIDIA Corporation, GDC
Gummaraju J, Rosenblum M (2005) Stream Programming on General-Purpose Processors. In: Proceedings of the 38th annual IEEE/ACM International Symposium on Microarchitecture, Barcelona, Spain
Jodoin PM, Mignotte M (2006) Markovian segmentation and parameter estimation on graphics hardware. Journal of Electronic Imaging 15(3):033005-1–033005-15
Jodoin PM, St Amour JF, Mignotte M (2005) Unsupervised Markovian segmentation on graphics hardware. Pattern Recognition and Image Analysis 3687:444–454
Mohamed A, Zacharaki EI, Shen D, Davatzikos C (2006) Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Medical Image Analysis 10(5):752–763
Murino V, Castellani U, Fusiello A (2001) Disparity map restoration by integration of confidence in Markov random fields models. In: International Conference on Image Processing
Paulsen RR (2004) Statistical Shape Analysis of the Human Ear Canal with Application to In-the-Ear Hearing Aid Design. PhD thesis, Technical University of Denmark
Provost JN, Collet C, Rostaing P, Pérez P, Bouthemy P (2004) Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depth maps. Computer Vision and Image Understanding 93(2):155–174
de Putter S, van de Vosse FN, Gerritsen, FA, Laffargue F, Breeuwer M (2006) Computational mesh generation for vascular structures with deformable surfaces. International Journal of Computer Assisted Radiology and Surgery 1(1):39–49
Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8):712–721
Sigal IA, Hardisty MR, Whyne CM (2008) Mesh-morphing algorithms for specimen-specific finite element modeling. Journal of Biomechanics, 1381–1389.
Winkler G (2006) Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Springer, Second edition
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Seiler, C., Büchler, P., Nolte, LP., Reyes, M., Paulsen, R. (2009). Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware. In: Magnenat-Thalmann, N., Zhang, J., Feng, D. (eds) Recent Advances in the 3D Physiological Human. Springer, London. https://doi.org/10.1007/978-1-84882-565-9_9
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DOI: https://doi.org/10.1007/978-1-84882-565-9_9
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