Variational problems on flows of diffeomorphisms for image matching
Quarterly of Applied Mathematics
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A template free approach to volumetric spatial normalization of brain anatomy
Pattern Recognition Letters
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
On Manifold Structure of Cardiac MRI Data: Application to Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Journal of Computational and Applied Mathematics
Discovering Modes of an Image Population through Mixture Modeling
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Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Unbiased atlas formation via large deformations metric mapping
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Efficient population registration of 3d data
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
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
Construction of neuroanatomical shape complex atlas from 3D brain MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Random forest-based manifold learning for classification of imaging data in dementia
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Foundations and Trends® in Computer Graphics and Vision
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
3D anatomical shape atlas construction using mesh quality preserved deformable models
MeshMed'12 Proceedings of the 2012 international conference on Mesh Processing in Medical Image Analysis
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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This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain images is proposed. We hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.e. a parametrization of the set of images. The approach builds on recent advances in manifold learning that allow to uncover nonlinear trends in data. We combine this manifold learning with distance measures between images that capture shape, in order to learn the underlying structure of a database of brain images. The proposed method is generative. New images can be created from the manifold parametrization and existing images can be projected onto the manifold. By measuring projection distance of a held out set of brain images we evaluate the fit of the proposed manifold model to the data and we can compute statistical properties of the data using this manifold structure. We demonstrate this technology on a database of 436 MR brain images.