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Related Experiment Video

Updated: Mar 29, 2026

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

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Published on: December 4, 2020

Entropy-based particle correspondence for shape populations.

Ipek Oguz¹, Josh Cates², Manasi Datar²

¹University of Iowa, Iowa City, IA, USA. ipek-oguz@uiowa.edu.

International Journal of Computer Assisted Radiology and Surgery

|December 10, 2015

Summary

This summary is machine-generated.

This study introduces an entropy-based framework for establishing accurate point correspondence in statistical shape analysis. The method is parameterization-free, efficient, and handles complex geometries for improved anatomical studies.

Keywords:

Correspondence Entropy Shape analysis

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Spatial Separation of Molecular Conformers and Clusters

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Published on: January 9, 2014

Area of Science:

Medical Image Analysis
Computational Anatomy
Statistical Shape Analysis

Background:

Accurate correspondence across anatomical structures is crucial for statistical shape analysis.
Understanding anatomical changes in growth and disease relies on robust shape analysis techniques.

Purpose of the Study:

To present an entropy-based correspondence framework for groupwise, point-based correspondence of surface populations.
To develop a parameterization-free and computationally efficient method for anatomical correspondence.

Main Methods:

An entropy-based framework for groupwise surface correspondence.
Parameterization-free computation for enhanced flexibility.
Extensions to handle complex geometries and application-driven metrics.

Main Results:

Demonstrated application on synthetic and biological datasets.
Comparison of framework performance against existing correspondence techniques.
Validation of the method's effectiveness in various scenarios.

Conclusions:

The developed framework is highly flexible and adaptable.
It effectively handles diverse object topologies, including multi-object complexes and open surfaces.
The method accommodates complex geometric features like high curvature and thin structures.