Scale and the differential structure of images
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Active shape models—their training and application
Computer Vision and Image Understanding
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Inter Subject Registration of Functional and Anatomical Data Using SPM
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
A learning-based approach to evaluate registration success
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Coordinate-based versus structural approaches to brain image analysis
Artificial Intelligence in Medicine
Evaluating similarity measures for brain image registration
Journal of Visual Communication and Image Representation
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Although numerous methods to register brains of different individuals have been proposed, few work has been done to evaluate the performances of different registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the quality of the registration. Experiments have been conducted for 5 methods, through a database of 18 subjects. We focused more extensively on the registration of cortical landmarks that have a particular relevance in the context of anatomical-functional normalization. For global measures, results show that the quality of the registration is directly related to the transformation's degrees of freedom. However, local measures based on the matching of cortical sulci, did not make it possible to show significant differences between affine and non linear methods.