A noise-resistant and annotation-free supervoxel-based algorithm for rapid segmentation of multiphase X-ray images
File(s)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2025-04-01
Date Acceptance
2025-03-20
Citation
Advances in Geo-Energy Research, 2025, 16 (1), pp.50-59
ISSN
2207-9963
Publisher
Yandy Scientific Press
Start Page
50
End Page
59
Journal / Book Title
Advances in Geo-Energy Research
Volume
16
Issue
1
Copyright Statement
© The Author(s) 2025. Open Access This article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Subjects
contact angles
CONTACT-ANGLE
curvature
Energy & Fuels
FLOW
FLUID
Geology
Geosciences, Multidisciplinary
image processing
Physical Sciences
Science & Technology
supervoxel segmentation
Technology
X-ray images
Publication Status
Published
Date Publish Online
2024-03-24