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Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
https://doi.org/10.5194/se-13-1475-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-13-1475-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
Research article
 | 
27 Sep 2022
Research article |  | 27 Sep 2022

Detecting micro fractures: a comprehensive comparison of conventional and machine-learning-based segmentation methods

Dongwon Lee, Nikolaos Karadimitriou, Matthias Ruf, and Holger Steeb

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Cited

Latest update: 13 Dec 2024
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Short summary
This research article focuses on filtering and segmentation methods employed in high-resolution µXRCT studies for crystalline rocks, bearing fractures, or fracture networks, of very small aperture. Specifically, we focus on the identification of artificially induced (via quenching) fractures in Carrara marble samples. Results from the same dataset from all five different methods adopted were produced and compared with each other in terms of their output quality and time efficiency.
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