A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Globally constrained deformable models for 3D object reconstruction
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
An Electro-mechanical Model of the Heart for Cardiac Image Analysis
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Proceedings of the First International Workshop on Functional Imaging and Modeling of the Heart
Nonlinear Registration of Brain Images Using Deformable Models
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Epipolar Geometry of Opti-Acoustic Stereo Imaging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preliminary validation using in vivo measures of a macroscopic electrical model of the heart
IS4TM'03 Proceedings of the 2003 international conference on Surgery simulation and soft tissue modeling
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This article describes a process to include in a volumetric model various anatomical and mechanical information provided by different sources. Three stages are described, namely a mesh construction, the non-rigid deformation of the tetrahedral mesh into various volumetric images, and the rasterization procedure allowing the transfer of properties from a voxel grid to a tetrahedral mesh. The method is experimented on various imaging modalities, demonstrating its feasibility. By using a biomechanical model, we include physically-based a priori knowledge which should allow to better recover the cardiac motion from images.