Articles | Volume 8, issue 2
https://doi.org/10.5194/bg-8-279-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/bg-8-279-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
09 Feb 2011
Detection of pore space in CT soil images using artificial neural networks
M. G. Cortina-Januchs
Technical University of Madrid, Group for Automation in Signals and Communications, Madrid, Spain
University of Guanajuato, Electronic Engineering Department Guanajuato, Mexico
J. Quintanilla-Dominguez
Technical University of Madrid, Group for Automation in Signals and Communications, Madrid, Spain
University of Guanajuato, Electronic Engineering Department Guanajuato, Mexico
A. Vega-Corona
University of Guanajuato, Electronic Engineering Department Guanajuato, Mexico
A. M. Tarquis
Technical University of Madrid, Group for Automation in Signals and Communications, Madrid, Spain
D. Andina
Technical University of Madrid, Group for Automation in Signals and Communications, Madrid, Spain
Viewed
Total article views: 3,791 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 16 Aug 2010)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,633 | 1,982 | 176 | 3,791 | 151 | 118 |
- HTML: 1,633
- PDF: 1,982
- XML: 176
- Total: 3,791
- BibTeX: 151
- EndNote: 118
Total article views: 3,039 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 09 Feb 2011)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,338 | 1,582 | 119 | 3,039 | 134 | 114 |
- HTML: 1,338
- PDF: 1,582
- XML: 119
- Total: 3,039
- BibTeX: 134
- EndNote: 114
Total article views: 752 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 16 Aug 2010)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 295 | 400 | 57 | 752 | 17 | 4 |
- HTML: 295
- PDF: 400
- XML: 57
- Total: 752
- BibTeX: 17
- EndNote: 4
Cited
32 citations as recorded by crossref.
- Multifractal analysis of 3D images of tillage soil I. Torre et al. 10.1016/j.geoderma.2017.02.013
- X‐ray computed tomography–measured soil pore parameters as influenced by crop rotations and cover crops J. Singh et al. 10.1002/saj2.20105
- Improved segmentation of X-ray tomography data from porous rocks using a dual filtering approach D. Müter et al. 10.1016/j.cageo.2012.06.024
- Local 3D segmentation of soil pore space based on fractal properties using singularity maps J. Martín-Sotoca et al. 10.1016/j.geoderma.2016.11.029
- Scaling properties of binary and greyscale images in the context of X-ray soil tomography I. Torre et al. 10.1016/j.geoderma.2020.114205
- Processing of rock core microtomography images: Using seven different machine learning algorithms S. Chauhan et al. 10.1016/j.cageo.2015.10.013
- Micro-structural Evolution of Granite Residual Soil under External Loading Based on X-ray Micro-computed Tomography Y. Zhao et al. 10.1007/s12205-021-0803-5
- Combining global and local scaling methods to detect soil pore space J. Martín-Sotoca et al. 10.1016/j.gexplo.2017.06.017
- Pore detection in 3‐D CT soil samples through an improved sub‐segmentation method B. Ojeda‐Magaña et al. 10.1111/ejss.12728
- Automatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN J. Quintanilla-Domínguez et al. 10.1080/1931308X.2013.838070
- Evaluation of X-ray computed tomography for quantifying macroporosity of loamy pasture soils M. Rab et al. 10.1016/j.geoderma.2013.08.037
- New segmentation method based on fractal properties using singularity maps J. Martín-Sotoca et al. 10.1016/j.geoderma.2016.09.005
- Using computed tomography images to characterize the effects of soil compaction resulting from large machinery on three-dimensional pore characteristics in an opencast coal mine dump Y. Feng et al. 10.1007/s11368-018-2130-0
- Application of sub-segmentation enhancement in pore detection in soil CT images M. ARREGUIN-JUÁREZ et al. 10.35429/JTI.2021.22.8.9.19
- A neighborhood median weighted fuzzy c-means method for soil pore identification Q. HAN et al. 10.1016/S1002-0160(21)60034-6
- Identification of pore spaces in 3D CT soil images using PFCM partitional clustering B. Ojeda-Magaña et al. 10.1016/j.geoderma.2013.11.005
- Sensitivity of Digital Rock Method for Pore-Space Estimation to Heterogeneity in Carbonate Formations R. Sharma et al. 10.2118/205006-PA
- Characterization of macropore structure of remolded loess and analysis of hydraulic conductivity anisotropy using X-ray computed tomography technology H. Wang et al. 10.1007/s12665-021-09405-z
- Multiscaling properties of soil images I. Torre et al. 10.1016/j.biosystemseng.2016.11.006
- Evaluating Petrophysical Properties Using Digital Rock Physics Analysis: A CO2 Storage Feasibility Study of Lithuanian Reservoirs S. Malik et al. 10.3390/app142310826
- Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study S. Chauhan et al. 10.5194/se-7-1125-2016
- Experimental investigations of hydraulic and mechanical properties of granite residual soil improved with cement addition Y. Zhao et al. 10.1016/j.conbuildmat.2021.126016
- Impacts of Liquid Fractions from Two Solid–Liquid Separation Technologies on the Soil Porosity, Ammonia, and Greenhouse Gas Emissions S. Wang et al. 10.3390/agronomy14010186
- A Simplified Convolutional Network for Soil Pore Identification Based on Computed Tomography Imagery Q. Han et al. 10.2136/sssaj2019.04.0119
- Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: Semantic segmentation using a 2.5D CNN A. Roslin et al. 10.1016/j.mineng.2023.108027
- Mechanical behaviour of disintegrated carbonaceous mudstone under stress and cyclic drying/wetting L. Zeng et al. 10.1016/j.conbuildmat.2021.122656
- X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods P. Guntoro et al. 10.3390/min9030183
- Soil pore structure and its research methods: A review N. Wang & T. Zhang 10.17221/64/2023-SWR
- Determination of water flow through clayey slurries using computed micro-tomography M. Ito & S. Azam 10.1144/qjegh2016-089
- Mesomechanics characteristics of soil reinforcement by plant roots Y. Zhou & X. Wang 10.1007/s10064-018-1370-y
- The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing G. Garfi et al. 10.1007/s11242-019-01374-z
- Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques A. Rabbani et al. 10.1029/2020WR029472
32 citations as recorded by crossref.
- Multifractal analysis of 3D images of tillage soil I. Torre et al. 10.1016/j.geoderma.2017.02.013
- X‐ray computed tomography–measured soil pore parameters as influenced by crop rotations and cover crops J. Singh et al. 10.1002/saj2.20105
- Improved segmentation of X-ray tomography data from porous rocks using a dual filtering approach D. Müter et al. 10.1016/j.cageo.2012.06.024
- Local 3D segmentation of soil pore space based on fractal properties using singularity maps J. Martín-Sotoca et al. 10.1016/j.geoderma.2016.11.029
- Scaling properties of binary and greyscale images in the context of X-ray soil tomography I. Torre et al. 10.1016/j.geoderma.2020.114205
- Processing of rock core microtomography images: Using seven different machine learning algorithms S. Chauhan et al. 10.1016/j.cageo.2015.10.013
- Micro-structural Evolution of Granite Residual Soil under External Loading Based on X-ray Micro-computed Tomography Y. Zhao et al. 10.1007/s12205-021-0803-5
- Combining global and local scaling methods to detect soil pore space J. Martín-Sotoca et al. 10.1016/j.gexplo.2017.06.017
- Pore detection in 3‐D CT soil samples through an improved sub‐segmentation method B. Ojeda‐Magaña et al. 10.1111/ejss.12728
- Automatic Detection of Microcalcifications in ROI Images Based on PFCM and ANN J. Quintanilla-Domínguez et al. 10.1080/1931308X.2013.838070
- Evaluation of X-ray computed tomography for quantifying macroporosity of loamy pasture soils M. Rab et al. 10.1016/j.geoderma.2013.08.037
- New segmentation method based on fractal properties using singularity maps J. Martín-Sotoca et al. 10.1016/j.geoderma.2016.09.005
- Using computed tomography images to characterize the effects of soil compaction resulting from large machinery on three-dimensional pore characteristics in an opencast coal mine dump Y. Feng et al. 10.1007/s11368-018-2130-0
- Application of sub-segmentation enhancement in pore detection in soil CT images M. ARREGUIN-JUÁREZ et al. 10.35429/JTI.2021.22.8.9.19
- A neighborhood median weighted fuzzy c-means method for soil pore identification Q. HAN et al. 10.1016/S1002-0160(21)60034-6
- Identification of pore spaces in 3D CT soil images using PFCM partitional clustering B. Ojeda-Magaña et al. 10.1016/j.geoderma.2013.11.005
- Sensitivity of Digital Rock Method for Pore-Space Estimation to Heterogeneity in Carbonate Formations R. Sharma et al. 10.2118/205006-PA
- Characterization of macropore structure of remolded loess and analysis of hydraulic conductivity anisotropy using X-ray computed tomography technology H. Wang et al. 10.1007/s12665-021-09405-z
- Multiscaling properties of soil images I. Torre et al. 10.1016/j.biosystemseng.2016.11.006
- Evaluating Petrophysical Properties Using Digital Rock Physics Analysis: A CO2 Storage Feasibility Study of Lithuanian Reservoirs S. Malik et al. 10.3390/app142310826
- Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study S. Chauhan et al. 10.5194/se-7-1125-2016
- Experimental investigations of hydraulic and mechanical properties of granite residual soil improved with cement addition Y. Zhao et al. 10.1016/j.conbuildmat.2021.126016
- Impacts of Liquid Fractions from Two Solid–Liquid Separation Technologies on the Soil Porosity, Ammonia, and Greenhouse Gas Emissions S. Wang et al. 10.3390/agronomy14010186
- A Simplified Convolutional Network for Soil Pore Identification Based on Computed Tomography Imagery Q. Han et al. 10.2136/sssaj2019.04.0119
- Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: Semantic segmentation using a 2.5D CNN A. Roslin et al. 10.1016/j.mineng.2023.108027
- Mechanical behaviour of disintegrated carbonaceous mudstone under stress and cyclic drying/wetting L. Zeng et al. 10.1016/j.conbuildmat.2021.122656
- X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods P. Guntoro et al. 10.3390/min9030183
- Soil pore structure and its research methods: A review N. Wang & T. Zhang 10.17221/64/2023-SWR
- Determination of water flow through clayey slurries using computed micro-tomography M. Ito & S. Azam 10.1144/qjegh2016-089
- Mesomechanics characteristics of soil reinforcement by plant roots Y. Zhou & X. Wang 10.1007/s10064-018-1370-y
- The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing G. Garfi et al. 10.1007/s11242-019-01374-z
- Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques A. Rabbani et al. 10.1029/2020WR029472
Saved (final revised paper)
Latest update: 02 Apr 2025
Special issue
Altmetrics
Final-revised paper
Preprint