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Acta Geotechnica

, Volume 14, Issue 1, pp 1–18 | Cite as

Reconstructing granular particles from X-ray computed tomography using the TWS machine learning tool and the level set method

  • Zhengshou Lai
  • Qiushi ChenEmail author
Research Paper
  • 160 Downloads

Abstract

X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.

Keywords

3D particle morphology Level set Machine learning Shape reconstruction X-ray computed tomography 

Notes

Acknowledgements

The authors would like to acknowledge the financial support provided by the NASA SC Space Consortium Grant (No. NNX15AL49H).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Glenn Department of Civil EngineeringClemson UniversityClemsonUSA

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