Active shape models—their training and application
Computer Vision and Image Understanding
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
A fast level set based algorithm for topology-independent shape modeling
Journal of Mathematical Imaging and Vision - Special issue on topology and geometry in computer vision
Computational anatomy: an emerging discipline
Quarterly of Applied Mathematics - Special issue on current and future challenges in the applications of mathematics
Multi-Object Analysis of Volume, Pose, and Shape Using Statistical Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Analysis of Elastic Curves in Euclidean Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elastic Geodesic Paths in Shape Space of Parameterized Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
A Reparameterisation Based Approach to Geodesic Constrained Solvers for Curve Matching
International Journal of Computer Vision
SUPIR: surface uncertainty-penalized, non-rigid image registration for pelvic CT imaging
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Elastic shape matching of parameterized surfaces using square root normal fields
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Statistical analysis of manual segmentations of structures in medical images
Computer Vision and Image Understanding
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We consider the task of computing shape statistics and classification of 3D anatomical structures (as continuous, parameterized surfaces). This requires a Riemannian metric that allows re-parameterizations of surfaces by isometries, and computations of geodesics. This allows computing Karcher means and covariances of surfaces, which involves optimal re-parameterizations of surfaces and results in a superior alignment of geometric features across surfaces. The resulting means and covariances are better representatives of the original data and lead to parsimonious shape models. These two moments specify a normal probability model on shape classes, which are used for classifying test shapes into control and disease groups.We demonstrate the success of this model through improved random sampling and a higher classification performance. We study brain structures and present classification results for Attention Deficit Hyperactivity Disorder. Using the mean and covariance structure of the data, we are able to attain an 88% classification rate.