Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

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 Methods (FEM), etc. 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 paper 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 im- plemented on the Graphics Processor Unit (GPU).
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https://hal.inria.fr/hal-00813746
Contributeur : Project-Team Asclepios <>
Soumis le : mardi 16 avril 2013 - 11:00:11
Dernière modification le : jeudi 11 janvier 2018 - 16:24:49

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  • HAL Id : hal-00813746, version 1

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Christof Seiler, P. Büchler, L.-P. Nolte, R. Paulsen, Mauricio Reyes. Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware. N. Magnenat-Thalmann, J. J. Zhang & Feng, D. D. Recent Advances in the 3D Physiological Human, Springer, pp.133-148, 2009. 〈hal-00813746〉

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