International Journal of Computer Vision
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Analyzing the Neocortical Fine-Structure
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A MRF Based Random Graph Modelling the Human Cortical Topography
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
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
Gyral Parcellation of the Cortical Surface Using Geodesic Voronoï Diagrams
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Retrospective Evaluation of Inter-subject Brain Registration
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
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Connectivity-based parcellation of the cortical mantle using q-ball diffusion imaging
Journal of Biomedical Imaging - Recent Advances in Neuroimaging Methodology
Hi-index | 0.00 |
A basic issue in neurosciences is to look for possible relationships between brain architecture and cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore, the relationships between architectural and functional brain organizations are difficult to study when analyzing neuroimaging experiments. This paper advocates that the design of new brain image analysis methods inspired by the structural strategies often used in computer vision may provide better ways to address these relationships. The key point underlying this new framework is the conversion of the raw images into structural representations before analysis. These representations are made up of data-driven elementary features like activated clusters, cortical folds or fiber bundles. Two classes of methods are introduced. Inference of structural models via matching across a set of individuals is described first. This inference problem is illustrated by the group analysis of functional statistical parametric maps (SPMs). Then, the matching of new individual data with a priori known structural models is described, using the recognition of the cortical sulci as a prototypical example.