Detection and quantification of pore, solid and gravel spaces in CT images of a 3D soil sample

Ojeda-Maga�a, B.; Ruelas, R.; Quintanilla-Dom�nguez, J.; Robledo-Hern�ndez, J. G.; Sturrock, C. J.; Mooney, S. J.; Tarquis, A. M.

Abstract

© 2020 Elsevier Inc. In this study, we scanned the core of a cylindrical soil sample (60mm diameter and 100mm height) by X-ray Computed Tomography (CT) producing 300 consecutive 2D digital images with 16-bit gray level depth and a resolution of 32 microns (image size 676 × 676 pixels). The aim of this work was to determine the geometry and spatial distribution of the elements in a sample, related in this case to pore, solid and gravel, inside each 2D image for the latter reconstruction of the corresponding 3D approximation of the elements using the total set of 300 soil images. Therefore, it was possible to determine the relative percentage of each element present in each 2D image and, correspondingly, the structure and total percentage in the 3D reconstruction. The identification of elements in the 2D image slices was very well accomplished using three standard segmentation algorithms: k-Means, Fuzzy c-Means and Otsu multilevel. In order to compare and evaluate the quality of results, a non-uniformity (NU) measure was applied such that low values were indicative of homogeneous regions. Due to the depth of the greyscale of the images, the results were very similar with comparable statistics and homogeneity (NU values) among the detected materials of the three algorithms. That suggests that the pore, solid and gravel spaces were very well identified, and this is reflected through their connectivity in the 3D reconstruction. Additionally, the gray level depth was reduced to 8 bits and the same study was undertaken. In this case, the quality of results was comparable to the previous ones, as the number of elements and NU values were very close. However, this also depends largely on the high resolution of the images. Thereby, the soil sample of this work was very well characterized using the simplest and most common algorithms for image segmentation thanks to the high contrast and resolution, and regardless the depth of the grey-level.

Citation

Ojeda-Magaña, B., Ruelas, R., Quintanilla-Domínguez, J., Robledo-Hernández, J. G., Sturrock, C. J., Mooney, S. J., & Tarquis, A. M. (2020). Detection and quantification of pore, solid and gravel spaces in CT images of a 3D soil sample. Applied Mathematical Modelling, 85, 360-377. https://doi.org/10.1016/j.apm.2020.04.021

• Journal Article Type: Article
• Acceptance Date: Apr 28, 2020
• Online Publication Date: May 11, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: May 1, 2020
• Publicly Available Date: May 12, 2022
• Journal: Applied Mathematical Modelling
• Print ISSN: 0307-904X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 85
• Pages: 360-377
• DOI: https://doi.org/10.1016/j.apm.2020.04.021
• Keywords: 3D soil image analysis; X-Ray CT; Realistic 3-D reconstruction; Porosity; Images segmentation
• Public URL: https://nottingham-repository.worktribe.com/output/4370237
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0307904X20302146