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Entropy-based particle correspondence for shape populations



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.


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.


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


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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This work is part of the National Alliance for Medical Image Computing (NAMIC), funded through the NIH Roadmap for Medical Research, U54-EB005149. This research is also partially funded by UNC NDRC HD03110.

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Correspondence to Ipek Oguz.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008(5). All institutional and national guidelines for the care and use of laboratory animals were followed.

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Informed consent was obtained from all patients for being included in the study.

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Oguz, I., Cates, J., Datar, M. et al. Entropy-based particle correspondence for shape populations. Int J CARS 11, 1221–1232 (2016).

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  • Correspondence
  • Shape analysis
  • Entropy