Unsupervised learning of an atlas from unlabeled point-sets (2004)
| Venue: | IEEE Trans. Pattern Anal. Mach. Intell |
| Citations: | 35 - 1 self |
BibTeX
@ARTICLE{Chui04unsupervisedlearning,
author = {Haili Chui and Anand Rangarajan},
title = {Unsupervised learning of an atlas from unlabeled point-sets},
journal = {IEEE Trans. Pattern Anal. Mach. Intell},
year = {2004},
volume = {26},
pages = {160--172}
}
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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 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 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 a mean shape from nine hand-segmented 2D corpus callosum data sets. 1
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