|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4953317||1443004||2018||9 صفحه PDF||سفارش دهید||دانلود کنید|
- Novel Riemannian framework for statistical shape analysis that is able to account for the nonlinearity in shape variation.
- Lie group structure with closed-form expressions guaranteeing numerical efficiency.
- Results on the open-access OAI and FAUST datasets demonstrate superior performance over state-of-the-art.
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: Medical Image Analysis - Volume 43, January 2018, Pages 1-9