Active shape models—their training and application
Computer Vision and Image Understanding
Statistical Study on Cortical Sulci of Human Brains
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Non-linear Local Registration of Functional Data
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Recognition of Cortical Sulci Using a Congregation of Neural Networks
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
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In the context of neuroimaging probabilistic atlases, we propose a statistical framework to model the inter-individual variability of pairs of sulci with respect to their relative position and orientation. The approach extends previous work [3], and relies on the statistical analysis of a training set. We first define an appropriate data representation, through an observation vector, in order to build a consistent training population, on which we then apply a normed principal components analysis (normed-PCA). Experiments have been performed on pairs of major sulci extracted from 18 MR images.