Abstract
A new fully automated shape learning method is presented. It is based on clustering a shape training set in the original shape space and performing a Procrustes analysis on each cluster to obtain a cluster prototype and information about shape variation. As a direct application of our shape learning method, a 17-structure shape model of brain substructures was computed from MR image data, an eigen-shape model was automatically derived. Our approach can serve as an automated substitute to the tedious and time-consuming manual shape analysis.1
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© 1999 Springer-Verlag Berlin Heidelberg
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Duta, N., Sonka, M., Jain, A.K. (1999). Learning Shape Models from Examples Using Automatic Shape Clustering and Procrustes Analysis. In: Kuba, A., Šáamal, M., Todd-Pokropek, A. (eds) Information Processing in Medical Imaging. IPMI 1999. Lecture Notes in Computer Science, vol 1613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48714-X_31
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DOI: https://doi.org/10.1007/3-540-48714-X_31
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