Abstract
In this paper we propose a new method for shape analysis based on the depth-ordering of shapes. We use this depth-ordering to non-parametrically define depth with respect to a normal control population. This allows us to quantify differences with respect to "normality". We combine this approach with a permutation test allowing it to test for localized shape differences. The method is evaluated on a synthetically generated striatum dataset as well as on a real caudate dataset. © 2014 Springer International Publishing.
Cite
CITATION STYLE
Hong, Y., Gao, Y., Niethammer, M., & Bouix, S. (2014). Depth-based shape-analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8675 LNCS, pp. 17–24). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_3
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