Computational anatomy: an emerging discipline
Quarterly of Applied Mathematics - Special issue on current and future challenges in the applications of mathematics
Discovering Modes of an Image Population through Mixture Modeling
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
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
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Manifold learning for biomarker discovery in MR imaging
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Combining morphological information in a manifold learning framework: application to neonatal MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Manifold learning for image-based breathing gating with application to 4D ultrasound
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Groupwise registration with sharp mean
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Whole heart segmentation of cardiac MRI using multiple path propagation strategy
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Hierarchical manifold learning
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Sparse projections of medical images onto manifolds
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Geodesic registration methods have been used to solve the large deformation registration problems, which are hard to solve with conventional registration methods. However, analytically defined geodesics may not coincide with anatomically optimal paths of registration. In this paper we propose a novel and efficient method for large deformation registration by learning the underlying structure of the data using a manifold learning technique. In this method a large deformation between two images is decomposed into a series of small deformations along the shortest path on the graph that approximates the metric structure of data. Furthermore, the graph representation allows us to estimate the optimal group template by minimizing geodesic distances. We demonstrate the advantages of the proposed method with synthetic 2D images and real 3D mice brain volumes.