A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Matching with Free Extremities
Journal of Mathematical Imaging and Vision
3-D diffeomorphic shape registration on hippocampal data sets
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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One of the key challenges in deformable shape modeling is the problem of estimating a meaningful average or mean shape from a set of unlabeled shapes. We present a new joint clustering and matching algorithm that is capable of computing such a mean shape from multiple shape samples which are represented by unlabeled point-sets. An iterative bootstrap process is used wherein multiple shape sample point-sets are non-rigidly deformed to the emerging mean shape, with subsequent estimation of the mean shape based on these non-rigid alignments. The process is entirely sym-metric with no bias toward any of the original shape sam-ple point-sets. We believe that this method can be especially useful for creating atlases of various shapes present in med-ical images. We have applied the method to create a mean shape from nine hand-segmented 2D corpus callosum data sets.