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Entropy-based Particle Correspondence for Shape Populations

1University of Iowa, Iowa City, IA, USA.
2University of Utah, Salt Lake City, UT, USA.
3University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Publication Date:
Int J Comput Assist Radiol Surg
Volume Number:
Issue Number:
Int J Comput Assist Radiol Surg. 2016 Jul;11(7):1221-32.
PubMed ID:
Correspondence, Entropy, Shape analysis
Appears in Collections:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Oguz I., Cates J., Datar M., Paniagua B., Fletcher T., Vachet C., Styner M., Whitaker R. Entropy-based Particle Correspondence for Shape Populations. Int J Comput Assist Radiol Surg. 2016 Jul;11(7):1221-32. PMID: 26646417. PMCID: PMC4899300.
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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 group-wise 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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