Abstract：
:We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidean structure. A key advantage of our framework is that statistics in a manifold shape space becomes numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidean approach in terms of shape-based classification of morphological disorders.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
von Tycowicz C,Ambellan F,Mukhopadhyay A,Zachow Sdoi
10.1016/j.media.2017.09.004subject
Has Abstractpub_date
2018-01-01 00:00:00pages
1-9eissn
1361-8415issn
1361-8423pii
S1361-8415(17)30135-4journal_volume
43pub_type
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