@article{Ye2025, 
author = {Shanlin Ye and Xianzhi Song and Zhuangzhuang Ma and Yang Gao and Linqi Zhu and Mengmeng Zhou and Lizhi Xiao and Gege Wen and Branko Bijeljic and Martin J. Blunt},
title = {A noise-resistant and annotation-free supervoxel-based algorithm for rapid segmentation of multiphase X-ray images},
year = {2025},
journal = {Advances in Geo-Energy Research},
volume = {16},
number = {1},
pages = {50-59},
keywords = {X-ray images, image processing, supervoxel segmentation, curvature, contact angles},
url = {https://www.sciopen.com/article/10.46690/ager.2025.04.06},
doi = {10.46690/ager.2025.04.06},
abstract = {This study introduces a three-dimensional supervoxel segmentation method to accurately separate solid and fluid phases in X-ray images of porous materials, with applications in energy research. Compared with intelligent segmentation algorithms requiring model training, the proposed method operates as a ready-to-use solution with significantly enhanced efficiency. When benchmarked against conventional approaches such as watershed transformation, our technique demonstrates superior segmentation accuracy. Tested on porous rock and gas diffusion layers under varying wettability, it accurately quantifies fluid saturation, interfacial area, curvature, and contact angles-key parameters for enhanced oil recovery, CO2 storage, and hydrogen fuel cells. The proposed three-dimensional segmentation method is noise-resistant and annotation-free, improving both the accuracy and efficiency of segmenting diverse micro-structural material datasets and providing reliable measurements of their geometric characteristics.}
}