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Otsu’s Algorithm in the Segmentation of Pore Space in Soils Based on Tomographic Data

  • SOIL MINERALOGY AND MICROMORPHOLOGY
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Abstract—

Modern analytical research methods tend to automate the process and minimize any manual intervention. This also applies to image analysis of tomographic slices as part of the tomographic study of soils. To calculate morphometric parameters, a tomographic image must be segmented (divided into phases) via automated or manual thresholding. The problem in automated thresholding algorithms is insufficient accuracy when dealing with different data. The goal of this study is to apply one of the most common Otsu’s thresholding algorithms to tomographic images of different soils, to show how justified is its use, and to determine the causes and conditions of errors in automated segmentation. The research has been performed at the Laboratory of Soil Physics and Hydrogeology with the Dokuchaev Soil Science Institute. We have used the tomographic images of urban soils (Urbic Technosols), dark-gray soil (Chernic Phaeozem), and soddy-podzolic soil (Albic Retisol) scanned with different equipment. The automated segmentation results are compared to manual thresholding. The pore space of soils is used as the control X-ray contrast phase and the porosity and the amount of pores, as benchmarks. The results show that Otsu’s method most accurately works with large data, when the image artifacts (digital noise) are minimal or absent at all, which is a typical situation for the aggregates <1 mm. As for coarse-resolution surveys or noisy images typical of samples with a high X-ray absorption, automatic segmentation is highly undesirable.

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REFERENCES

  1. V. Cnudde and M. N. Boone, “High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications,” Earth-Sci. Rev. 123, 1–17 (2013). https://doi.org/10.1016/j.earscirev.2013.04.003

    Article  Google Scholar 

  2. V. Cnudde, B. Masschaele, M. Dierick, J. Vlassenbroeck, L. van Hoorebeke, and P. Jacobs, “Recent progress in X-ray CT as a geosciences tool,” Appl. Geochem. 21 (5), 826–832 (2006).

    Article  Google Scholar 

  3. K. M. Gerke, T. O. Sizonenko, M. V. Karsanina, E. V. Lavrukhin, V. V. Abashkin, and D. V. Korost, “Improving watershed-based pore-network extraction method using maximum inscribed ball pore-body positioning,” Adv. Water Res. 140, 103576 (2020). https://doi.org/10.1016/j.advwatres.2020.103576

    Article  Google Scholar 

  4. K. M. Gerke, R. V. Vasilyev, S. Khirevich, D. Collins, M. V. Karsanina, T. O. Sizonenko, D. V. Korost, S. Lamontagne, and D. Mallants, “Finite-difference method Stokes solver (FDMSS) for 3D pore geometries: software development, validation and case studies,” Comput. Geosci. 114, 41–58 (2018). https://doi.org/10.1016/j.cageo.2018.01.005

    Article  Google Scholar 

  5. K. M. Gerke, M. V. Karsanina, and D. Mallants, “Universal stochastic multiscale image fusion: an example application for shale rock,” Sci. Rep. 5, 15880 (2015). https://doi.org/10.1038/srep33086

    Article  Google Scholar 

  6. K. M. Gerke, M. V. Karsanina, and E. V. Skvortsova, “Description and reconstruction of the soil pore space using correlation functions,” Eurasian Soil Sci. 45, 861–872 (2012). https://doi.org/10.1134/S1064229312090049

    Article  Google Scholar 

  7. K. M. Gerke, M. V. Karsanina, and R. Katsman, “Calculation of tensorial flow properties on pore level: exploring the influence of boundary conditions on the permeability of three-dimensional stochastic reconstructions,” Phys. Rev. 100, 053312 (2019).

    Google Scholar 

  8. K. M. Gerke, E. B. Skvortsova, and D. V. Korost, “Tomographic method of studying soil pore space: current perspectives and results for some Russian soils,” Eurasian Soil Sci. 45, 700–709 (2012). https://doi.org/10.1134/S1064229312070034

    Article  Google Scholar 

  9. S. N. Gorbov, K. N. Abrosimov, O. S. Bezuglova, E. B. Skvortsova, K. A. Romanenko, and S. S. Tagiverdiev, “Use of tomographic methods for the study of urban soil properties,” in Proceedings of the 9th SUITMA Congr. “Urbanization: Challenge and Opportunity for Soil Functions and Ecosystem Services,” Moscow (Springer, Cham, 2018), pp. 249–259.

  10. S. N. Gorbov, K. N. Abrosimov, O. S. Bezuglova, E. V. Skvortsova, and S. S. Tagiverdiev, “Microtomography research of physical properties of urban soil,” IOP Conf. Ser.: Earth Environ. Sci. 368, 012015 (2019). https://doi.org/10.1088/1755-1315/368/1/012015

  11. S. M. Hapca, A. N. Houston, W. Otten, and P. C. Baveye, “New local thresholding method for soil images by minimizing grayscale intra-class variance,” Vadose Zone J. 12 (3), (2013).

  12. P. Iassonov, T. Gebrenegus, and M. Tuller, “Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures,” Water Resour. Res. 45 (9), W09415 (2009). https://doi.org/10.1029/2009WR008087

    Article  Google Scholar 

  13. A. Kaestner, E. Lehmann, and M. Stampanoni, “Imaging and image processing in porous media research,” Adv. Water Res. 31 (9), 1174–1187 (2008).

    Article  Google Scholar 

  14. M. V. Karsanina, E. V. Lavrukhin, D. S. Fomin, A. V. Yudina, K. N. Abrosimov, and K. M. Gerke, “Compressing soil structural information into parameterized correlation functions,” Eur. J. Soil Sci., (2020). https://doi.org/10.1111/ejss.13025

  15. M. V. Karsanina, K. M. Gerke, E. V. Skvortsova, and D. Mallants, “Universal spatial correlation functions for describing and reconstructing soil microstructure,” PLoS One 10 (5), e0126515 (2015). https://doi.org/10.1371/journal.pone.0126515

    Article  Google Scholar 

  16. M. V. Karsanina, K. M. Gerke, E. V. Skvortsova, A. L. Ivanov, and D. Mallants, “Enhancing image resolution of soils by stochastic multiscale image fusion,” Geoderma 314, 138–145 (2018).

    Article  Google Scholar 

  17. F. Khan, F. Enzmann, M. Kersten, A. Wiegmann, and K. Steiner, “3D simulation of the permeability tensor in a soil aggregate on basis of nanotomographic imaging and LBE solver,” J. Soils Sediments 12 (1), 86–96 (2012).

    Article  Google Scholar 

  18. S. Khirevich and T. W. Patzek, “Behavior of numerical error in pore-scale lattice Boltzmann simulations with simple bounce-back rule: analysis and highly accurate extrapolation,” Phys. Fluids 30 (9), 093604 (2018).

    Article  Google Scholar 

  19. V. A. Kholodov, N. V. Yaroslavtseva, Yu. R. Farkhodov, V. P. Belobrov, S. A. Yudin, A. Ya. Aydiev, V. I. Lazarev, and A. S. Frid, “Changes in the ratio of aggregate fractions in humus horizons of chernozems in response to the type of their use,” Eurasian Soil Sci. 52, 162–170 (2019). https://doi.org/10.1134/S1064229319020066

    Article  Google Scholar 

  20. J. Koestel, “SoilJ: an ImageJ plug-in for the semiautomatic processing of three-dimensional X-ray images of soils,” Vadose Zone J. 17 (1), (2018).

  21. J. M. Köhne, S. Schlüter, and H.-J. Vogel, “Predicting solute transport in structured soil using pore network models,” Vadose Zone J. 10 (3), 1082–1096 (2011).

    Article  Google Scholar 

  22. E. V. Lavrukhin, K. A. Romanenko, K. N. Abrosimov, M. V. Karsanina, and K. M. Gerke, Soil Tillage Res., (2021) (in press).

  23. M. P. Lebedeva, D. L. Golovanov, V. A. Shishkov, A. L. Ivanov, and K. N. Abrosimov, “Microscopic and tomographic studies for interpreting the genesis of desert varnish and the vesicular horizon of desert soils in mongolia and the USA,” Bol. Soc. Geol. Mex. 71 (1), 21–42 (2019).

    Article  Google Scholar 

  24. A. Liernur, A. C. Schomburg, P. Turberg, C. Guenat, R.-C. Le Bayon, and P. Brunner, “Coupling X-ray computed tomography and freeze-coring for the analysis of fine-grained low-cohesive soils,” Geoderma 308, 171–186 (2017). https://doi.org/10.1016/j.geoderma.2017.08.010

    Article  Google Scholar 

  25. X. Miao, K. M. Gerke, and T. O. Sizonenko, “A new way to parameterize hydraulic conductances of pore elements: a step forward to create pore-networks without pore shape simplifications,” Adv. Water Res. 105, 162–172 (2017). https://doi.org/10.1016/j.advwatres.2017.04.021

    Article  Google Scholar 

  26. Morphometric Parameters Measured by Skyscan™ CT Analyzer Software (Bruker MicroCT, Kontich, 2012).

  27. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979).

    Article  Google Scholar 

  28. A. M. Petrovic, J. E. Siebert, and P. E. Rieke, “Soil bulk density analysis in three dimensions by computed tomographic scanning,” Soil Sci. Soc. Am. J. 46 (3), 445–450 (1982). https://doi.org/10.2136/sssaj1982.03615995004600030001x

    Article  Google Scholar 

  29. L. Pogosyan, A. Gastelum, B. Prado, J. Marquez, K. Abrosimov, K. Romanenko, and S. Sedov, “Morphogenesis and quantification of the pore space in a tephra-palaeosol sequence in Tlaxcala, central Mexico,” Soil Res. 57 (6), 559–565 (2019). https://doi.org/10.1071/SR18185

    Article  Google Scholar 

  30. K. A. Romanenko, K. N. Abrosimov, A. N. Kurchatova, and Rogov, V. V. “The experience of applying x-ray computer tomography to the study of microstructure of frozen ground and soils,” Earth’s Cryosphere 21 (4), 63–68 (2017).

    Google Scholar 

  31. S. Schlüter, U. Weller, and H.-J. Vogel, “Segmentation of X-ray microtomography images of soil using gradient masks,” Comput. Geosci. 36 (10), 1246–1251 (2010).

    Article  Google Scholar 

  32. E. V. Shein, E. B. Skvortsova, A. V. Dembovetskii, K. N. Abrosimov, L. I. Il’in, and N. A. Shnyrev, “Pore-size distribution in loamy soils: a comparison between microtomographic and capillarimetric determination methods,” Eurasian Soil Sci. 49, 315–325 (2016).

    Article  Google Scholar 

  33. E. B. Skvortsova, E. V. Shein, K. N. Abrosimov, K. A. Romanenko, A. V. Yudina, V. V. Klyueva, D. D. Khaidapova, and V. V. Rogov, “The impact of multiple freeze–thaw cycles on the microstructure of aggregates from a soddy-podzolic soil: a microtomographic analysis,” Eurasian Soil Sci. 51, 190–198 (2018).

    Article  Google Scholar 

  34. SkyScan CT-Analyser (“CTan”): User Manual (Bruker MicroCT, Kontich, 2020).

  35. H.-J. Vogel and K. Roth, “Quantitative morphology and network representation of soil pore structure,” Adv. Water Res. 24 (3–4), 233–242 (2001).

    Article  Google Scholar 

  36. W. Wang, A. N. Kravchenko, A. J. M. Smucker, and M. L. Rivers, “Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates,” Geoderma 162, 231–241 (2011).

    Article  Google Scholar 

  37. D. Wildenschild and A. P. Sheppard, “X-ray imaging and analysis techniques for quantifying porescale structure and processes in subsurface porous medium systems,” Adv. Water Res. 51, 217–246 (2013). https://doi.org/10.1016/j.advwatres.2012.07.018

    Article  Google Scholar 

  38. IUSS Working Group WRB, World Reference Base for Soil Resources 2014, Update 2015, International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, World Soil Resources Reports No. 106 (UN Food and Agriculture Organization, Rome, 2015).

    Google Scholar 

  39. Y. S. Yang, K. Y. Liu, S. Mayo, A. Tulloh, M. B. Clennell, and T. Q. Xiao, “A data-constrained modeling approach to sandstone microstructure characterization,” J. Petrol. Sci. Eng. 105, 76–83 (2013).

    Article  Google Scholar 

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ACKNOWLEDGMENTS

The work was performed using equipment of the joint access center “Functions and Properties of Soils and Soil Cover” with the V.V. Dokuchaev Soil Science Institute, Russian Academy of Sciences.

Funding

The work was supported by the Russian Science Foundation under projects nos. 19-74-10070 (imaging), 17-77-20072 (sampling of dark-gray soil), and 19-16-00053 (sampling of chernozem aggregates).

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Authors and Affiliations

  1. Dokuchaev Soil Science Institute, Pyzhevskii per. 7/2, 119017, Moscow, Russia

    K. N. Abrosimov, K. M. Gerke & D. V. Korost

  2. Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, ul. Bol’shaya Gruzinskaya 10/1, 123242, Moscow, Russia

    K. M. Gerke

  3. Lomonosov Moscow State University, 119991, Moscow, Russia

    I. N. Semenkov & D. V. Korost

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  1. K. N. Abrosimov
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Correspondence to K. N. Abrosimov.

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Translated by G. Chirikova

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Abrosimov, K.N., Gerke, K.M., Semenkov, I.N. et al. Otsu’s Algorithm in the Segmentation of Pore Space in Soils Based on Tomographic Data. Eurasian Soil Sc. 54, 560–571 (2021). https://doi.org/10.1134/S1064229321040037

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