On the foundations of relaxation labeling processes
Readings in computer vision: issues, problems, principles, and paradigms
The Perceptual Organization of Texture Flow: A Contextual Inference Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Mean template for tensor-based morphometry using deformation tensors
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Tensor-based brain surface modeling and analysis
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Anisotropic diffusion of tensor fields for fold shape analysis on surfaces
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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The cortical surface of the human brain is composed of folds that are juxtaposed alongside one another. Several methods have been proposed to study the shape of these folds, e.g., by first segmenting them on the cortical surface or by analysis via a continuous deformation of a common template. A major disadvantage of these methods is that, while they can localize shape differences, they cannot easily identify the directions in which they occur. The type of deformation that causes a fold to change in length is quite different from that which causes it to change in width. Furthermore, these two deformations may have a completely different biological interpretation. In this article we propose a method to analyze such deformations using directional filters locally adapted to the geometry of the folding pattern. Motivated by the texture flow literature in computer vision we recover flow fields that maintain a fixed angle with the orientation of folds, over a significant spatial extent. We then trace the flow fields to determine which correspond to the shape changes that are the most salient. Using the OASIS database, we demonstrate that in addition to known regions of atrophy, our method can find subtle but statistically significant shape deformations.