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Related Experiment Video

Updated: May 10, 2026

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

3D superalloy grain segmentation using a multichannel edge-weighted centroidal Voronoi tessellation algorithm.

Yu Cao¹, Lili Ju, Youjie Zhou

¹Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA. cao@cec.sc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 26, 2013

Summary

This summary is machine-generated.

A new multichannel edge-weighted centroidal Voronoi tessellation (MCEWCVT) algorithm accurately segments superalloy grains in 3D images. This robust method improves micro-structure analysis for materials science applications.

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3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

Published on: April 29, 2020

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Last Updated: May 10, 2026

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

Published on: April 29, 2020

Area of Science:

Materials Science
Image Analysis
Computational Materials Science

Background:

Accurate grain segmentation in 3D superalloy images is crucial for evaluating material properties.
Challenges include numerous grains, image degradation from carbides and noise, and non-homogeneous intensity.
Multichannel imaging captures varying appearances under different microscope settings, complicating segmentation.

Purpose of the Study:

To develop an effective and robust algorithm for segmenting superalloy grains from 3D multichannel images.
To address the challenges of complex micro-structures and imaging artifacts in superalloys.
To improve the accuracy and reliability of micro-structural analysis in materials engineering.

Main Methods:

Development of a multichannel edge-weighted centroidal Voronoi tessellation (MCEWCVT) algorithm.
Segmentation is achieved by minimizing an energy function balancing intensity similarity and boundary smoothness.
The algorithm processes 3D multichannel superalloy image data.

Main Results:

The MCEWCVT algorithm was quantitatively evaluated on a four-channel Ni-based superalloy dataset (IN100) against ground-truth segmentation.
Performance was further assessed on two synthesized four-channel superalloy datasets.
Qualitative and quantitative comparisons with 18 existing algorithms demonstrated MCEWCVT's effectiveness and robustness.

Conclusions:

The proposed MCEWCVT algorithm provides effective and robust segmentation of superalloy grains from 3D multichannel images.
This method enhances the analysis of superalloy micro-structures, leading to better material property evaluation.
MCEWCVT outperforms existing segmentation algorithms in accuracy and reliability for this challenging task.