Tycowicz, Christoph von ; Ambellan, Felix ; Mukhopadhyay, Anirban ; Zachow, Stefan (2018)
An efficient Riemannian statistical shape model using differential coordinates.
In: Medical Image Analysis, 43
doi: 10.1016/j.media.2017.09.004
Artikel, Bibliographie
Kurzbeschreibung (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 rep- resentation 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 ad- vantage of our framework is that statistics in a manifold shape space becomes numerically tractable im- proving 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 differ- ences. 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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Tycowicz, Christoph von ; Ambellan, Felix ; Mukhopadhyay, Anirban ; Zachow, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | An efficient Riemannian statistical shape model using differential coordinates |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Medical Image Analysis |
| Jahrgang/Volume einer Zeitschrift: | 43 |
| DOI: | 10.1016/j.media.2017.09.004 |
| URL / URN: | https://doi.org/10.1016/j.media.2017.09.004 |
| Kurzbeschreibung (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 rep- resentation 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 ad- vantage of our framework is that statistics in a manifold shape space becomes numerically tractable im- proving 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 differ- ences. 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. |
| Freie Schlagworte: | Statistical shape models (SSM), Medical diagnosis, Feature classifications |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 19 Jun 2019 11:08 |
| Letzte Änderung: | 28 Jul 2021 11:21 |
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