Unsupervised learning of an atlas from unlabeled point-sets.
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.
Chui
Haili
H
Medical Imaging Group, R2 Technologies, Sunnyvale, CA 94087, USA. hailic@r2tech.com
Rangarajan
Anand
A
Zhang
Jie
J
Morison Leonard
Christiana
C
D000465:Algorithms; D001185:Artificial Intelligence; D001271:Atlases as Topic; D016000:Cluster Analysis; D003196:Computer Graphics; D003337:Corpus Callosum / Q000033:anatomy & histology; D006624:Hippocampus / Q000033:anatomy & histology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014584:User-Computer Interface