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

Christof Seiler, Philippe Büchler, Lutz-Peter Nolte, Rasmus Paulsen, Mauricio Reyes
University of Bern, ARTORG Center, 3014 Bern, Switzerland
In Recent Advances in the 3D Physiological Human (2009), pp. 133-148

@article{seiler2009hierarchical,

   title={Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware},

   author={Seiler, C. and B{\”u}chler, P. and Nolte, L.P. and Reyes, M. and Paulsen, R.},

   journal={Recent Advances in the 3D Physiological Human},

   pages={133–148},

   year={2009},

   publisher={Springer}

}

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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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