Diffeomorphisms Groups and Pattern Matching in Image Analysis
International Journal of Computer Vision
Knowledge-based segmentation and labeling of brain structures from MRI images
Pattern Recognition Letters
A Digital Brain Atlas for Surgical Planning, Model-Driven Segmentation, and Teaching
IEEE Transactions on Visualization and Computer Graphics
Multisubject Non-rigid Registration of Brain MRI Using Intensity and Geometric Features
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Non-linear Cerebral Registration with Sulcal Constraints
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear registration using variational principle for mutual information
Pattern Recognition
Fast image registration by hierarchical soft correspondence detection
Pattern Recognition
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Landmark matching via large deformation diffeomorphisms
IEEE Transactions on Image Processing
Topology preserving deformable image matching using constrained hierarchical parametric models
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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A new non-rigid registration method combining image intensity and a priori shape knowledge of the objects in the image is proposed. This method, based on optical flow theory, uses a topology correction strategy to prevent topological changes of the deformed objects and the a priori shape knowledge to keep the object shapes during the deformation process. Advantages of the method over classical intensity based non-rigid registration are that it can improve the registration precision with the a priori knowledge and allows to segment objects at the same time, especially efficient in the case of segmenting adjacent objects having similar intensities. The proposed algorithm is applied to segment brain subcortical structures from 15 real brain MRI images and evaluated by comparing with ground truths. The obtained results show the efficiency and robustness of our method.