期刊
MEDICAL IMAGE ANALYSIS
卷 43, 期 -, 页码 1-9出版社
ELSEVIER
DOI: 10.1016/j.media.2017.09.004
关键词
Statistical shape analysis; Classification; Riemannian metrics; Principal geodesic analysis; Manifold valued statistics
类别
资金
- DFG [EH 422/1-1]
- BMBF project TOKMIS: Treating Osteoarthritis in Knee with Mimicked Inter-positional Spacer [01EC1406E]
- BMBF MODAL - MedLab
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. (C) 2017 Elsevier B.V. All rights reserved.
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