Landmark-based registration using features identified through differential geometry
Handbook of medical imaging
Hierarchical B-spline refinement
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Measurement of Image Velocity
Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First 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
A New Framework for Approximate Labeling via Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Efficient MRF deformation model for non-rigid image matching
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Nonrigid Image Registration Using Dynamic Higher-Order MRF Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Deformable Mosaicing for Whole-Body MRI
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Non-rigid Image Registration with Uniform Spherical Structure Patterns
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Dense Registration with Deformation Priors
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Non-rigid Image Registration with Uniform Gradient Spherical Patterns
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Primal/dual linear programming and statistical atlases for cartilage segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
4D ventricular segmentation and wall motion estimation using efficient discrete optimization
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Rolled fingerprint construction using MRF-based nonrigid image registration
IEEE Transactions on Image Processing
Solving MRFs with higher-order smoothness priors using hierarchical gradient nodes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Non-rigid image registration of brain magnetic resonance images using graph-cuts
Pattern Recognition
GPU accelerated normalized mutual information and B-spline transformation
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
Motion Coherent Tracking Using Multi-label MRF Optimization
International Journal of Computer Vision
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In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain. Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.