Stereo Without Epipolar Lines: A Maximum-Flow Formulation
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Real-time biomechanical simulation of volumetric brain deformation for image guided neurosurgery
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Determining Optical Flow
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Construction of a cardiac motion atlas from MR using non-rigid registration
FIMH'03 Proceedings of the 2nd international conference on Functional imaging and modeling of the heart
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Non-rigid image registration using graph-cuts
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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We present a graph-cuts based method for non-rigid medical image registration on brain magnetic resonance images. In this paper, the non-rigid medical image registration problem is reformulated as a discrete labeling problem. Based on a voxel-to-voxel intensity similarity measure, each voxel in the source image is assigned a displacement label, which represents a displacement vector indicating which position in the floating image it is spatially corresponding to. In the proposed method, a smoothness constraint based on the first derivative is used to penalize sharp changes in the adjacent displacement labels across voxels. The image registration problem is therefore modeled by two energy terms based on intensity similarity and smoothness of the displacement field. These energy terms are submodular and can be optimized by using the graph-cuts method via @a@?expansions, which is a powerful combinatorial optimization tool and capable of yielding either a global minimum or a local minimum in a strong sense. Using the realistic brain phantoms obtained from the Simulated Brain Database, we compare the registration results of the proposed method with two state-of-the-art medical image registration approaches: free-form deformation based method and demons method. In addition, the registration results are also compared with that of the linear programming based image registration method. It is found that the proposed method is more robust against different challenging non-rigid registration cases with consistently higher registration accuracy than those three methods, and gives realistic recovered deformation fields.