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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.07.2016 | Review Article

Entropy-based particle correspondence for shape populations

verfasst von: Ipek Oguz, Josh Cates, Manasi Datar, Beatriz Paniagua, Thomas Fletcher, Clement Vachet, Martin Styner, Ross Whitaker

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2016

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Abstract

Purpose

Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies.

Methods

In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics.

Results

We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques.

Conclusions

Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.

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http://www.nitrc.org/projects/shapeworks.

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Titel: Entropy-based particle correspondence for shape populations
verfasst von: Ipek Oguz
Josh Cates
Manasi Datar
Beatriz Paniagua
Thomas Fletcher
Clement Vachet
Martin Styner
Ross Whitaker
Publikationsdatum: 01.07.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 7/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-015-1319-6

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Abstract

Purpose

Methods

Results

Conclusions

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