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Unsupervised Learning of an Atlas from Unlabeled Point-Sets
February 2004 (vol. 26 no. 2)
pp. 160-172
Abstract—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 nonrigidly deformed to the emerging mean shape, with subsequent estimation of the mean shape based on these nonrigid alignments. The process is entirely symmetric with no bias toward any of the original shape sample point-sets. We believe that this method can be especially useful for creating atlases of various shapes present in medical images. We have applied the method to create mean shapes from nine hand-segmented 2D corpus callosum data sets and 10 hippocampal 3D point-sets.
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Index Terms:
Atlas, shapes, point-sets, correspondence, nonrigid, thin-plate splines, deterministic annealing, clustering, deformation, mixture models, EM algorithm, unsupervised learning.
Citation:
Haili Chui, Anand Rangarajan, Jie Zhang, Christiana Morison Leonard, "Unsupervised Learning of an Atlas from Unlabeled Point-Sets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 160-172, Jan. 2004, doi:10.1109/TPAMI.2004.1262178
