X‐ray Micro‐CT: How Soil Pore Space Description Can Be Altered by Image Processing

Abstract

A physically accurate conversion of the X-ray tomographic reconstructions of soil into pore networks requires a certain number of image processing steps. An important and much discussed issue in this field relates to segmentation, or distinguishing the pores from the solid, but pre-and post-segmentation noise reduction also affects the pore networks that are extracted. We used 15 two-dimensional simulated grayscale images to quantify the performance of three segmentation algorithms. These simulated images made ground-truth information available and a quantitative study feasible. The analyses were based on five performance indicators: misclassification error, non-region uniformity, and relative errors in porosity, conductance, and pore shape. Three levels of pre-segmentation noise reduction were tested, as well as two levels of post-segmentation noise reduction. Three segmentation methods were tested (two global and one local). For the local method, the threshold intervals were selected from two concepts: one based on the histogram shape and the other on the image visible-porosity value. The results indicate that pre-segmentation noise reduction significantly (p < 0.05) improves segmentation quality, but post-segmentation noise reduction is detrimental. The results also suggest that global and local methods perform in a similar way when noise reduction is applied. The local method, however, depends on the choice of threshold interval.Abbreviations: CT, computed tomography; GM, gradient masks; IK, indicator kriging; ME, misclassification error; NU, non-uniformity; PBA, porosity-based; RE_g, relative error in the pore shape; RE_K, relative error in conductance; RE_P, relative error in calculated porosity; RS, real soil; TH, threshold.Characterizing the soil's physical properties and understanding the resulting functions of the soil is of major importance for many agricultural and environmental issues. The soil is at the interface of most physical, chemical, and biological processes. In this regard, there is increasing interest in the use of noninvasive X-ray microtomography to obtain a microscopic three-dimensional view of the inner soil pore space (for a full description of the technology, see Landis and Keane, 2010). Several reviews (Taina et al., 2008;Helliwell et al., 2013;Wildenschild and Sheppard, 2013) have discussed the use of X-ray microtomography in soil and hydrological sciences. In these fields, the technique has been used at both the core scale (e.g.

Quantitative evaluation of pore characteristics of sodic soils reclaimed by flue gas desulphurization gypsum using X‐ray computed tomography

“…Although a large number of thresholding methods have been developed for segmentation of X‐ray CT image of soils, none of theoretical ‘standards' is generally accepted at present (Baveye et al, ; Iassonov et al, ; Marcelino et al, ; Smet et al, ; Taina I a, Heck RJ, Elliot TR., ; Wang et al, ). Due to the uncertain influences, such as unique characteristics of collected samples, resolution size of images, selection of thresholding algorithms, and unpredictable observer behavior, in the process of image segmentation, the previous studies often suggested different strategies for image segmentation after comparing various thresholding methods.…”

“…Previous review articles and research papers have offered sufficient study cases in exploring the reliability of applying X‐ray CT to quantify the soil pore characteristics (Cnudde & Boone, ; Haeffneff, ; Naveed et al, ; Taina et al ., 2008; Zhou, Mooney, & Peng, ). For example, studies have been widely carried out on segmentation of X‐ray CT images (Baveye et al, ; Iassonov, Gebrenegus, & Tuller, ; Schlüter, Sheppard, Brown, & Wildenschild, ) involving image resolution selection (Sleutel et al, ; Wildenschild et al, ), representative elementary area identification (San José Martínez, Caniego, García‐Gutiérrez, & Espejo, ), and optimal thresholding methods (Elliot & Heck, ; Smet, Plougonven, Leonard, Degré, & Beckers, ; Wang, Kravchenko, Smucker, & Rivers, ); quantification and reconstruction of the pore structure (Marcelino, Cnudde, Vansteelandt, & Carò, ) such as characterization of macropores (Garbout, Munkholm, & Hansen, ; Luo, Lin, & Schmidt, ), extraction of three‐dimensional (3D) typical pore parameters (Al‐Raoush & Willson, ; Luo, Lin, & Li, ), and assessing the spatial variability of soil structure (Carducci, Zinn, Rossoni, Heck, & Oliveira, ); as well as the relationship between pore characteristics and soil functions (Helliwell et al, ) including correlations with soil physical properties (Anderson, Gantzer, Boone, & Tully, ; Munkholm, Heck, & Deen, ) and explanation of the hydraulic conductivity (Luo, Lin, & Halleck, ; Naveed et al, ; Paradelo et al, ; Tracy et al, ). Although the majority of current case studies focus on naturally cultivated soils, there are still some reports confirming that images extracted from X‐ray CT are effective in quantifying pore characteristics of unnaturally soils such as reconstructed, degraded, and reclaimed soils (Dowuona, Taina, & Heck, ; Langmaack, Schrader, Rapp‐Bernhardt, & Kotzke, ; Li, Shao, & Jia, ; Wang, Guo, Bai, & Yang, ).…”

“…Image thresholding methodAlthough a large number of thresholding methods have been developed for segmentation of X-ray CT image of soils, none of theoretical 'standards' is generally accepted at present(Baveye et al, 2010;Iassonov et al, 2009;Marcelino et al, 2007;Smet et al, 2017;Taina TABLE 2Three-dimensional distribution of vertical and horizontal porosity for the three types of sodic soils before and after reclamation…”

Water Retention and Pore Size Distribution of a Biopolymeric‐Amended Loam Soil

“…The study of the impact on PSD is usually neglected in soil amendment experiments because it is difficult to quantify by classical field/laboratory methods, including direct or indirect measurement of PSD from water retention curve (WRC) measurements based on soil water content or geophysical measurements and hydrological inversion (as done by Jadoon et al, 2012; Busch et al, 2013; and Jonard et al, 2015) or WRC measured by pressure plate extractors (e.g., Bittelli and Flury, 2009); multistep outflow (e.g., Bayer et al, 2005; Hollenbeck and Jensen, 1998; Neyshabouri et al, 2013; Weihermüller et al, 2009); or the evaporation (e.g., Schindler et al, 2010; Žydelis et al, 2018), mercury intrusion (e.g., Webb, 2001), and nitrogen sorption (e.g., Kowalczyk et al, 2003) methods. As an alternative, noninvasive measurement techniques can be used, such as MicroCT (e.g., Koestel, 2018; Pohlmeier et al, 2018; Smet et al, 2017), synchrotron radiation and/or microtomography (e.g., Peth et al, 2008), or nuclear magnetic resonance relaxometry (NMRR) (e.g., Jaeger et al, 2009; Stingaciu et al, 2010).…”

Water Retention and Pore Size Distribution of a Biopolymeric-Amended Loam Soil

“…The study of the impact on PSD is usually neglected in soil amendment experiments because it is difficult to quantify by classical field/laboratory methods, including direct or indirect measurement of PSD from water retention curve (WRC) measurements based on soil water content or geophysical measurements and hydrological inversion (as done by Jadoon et al, 2012;Busch et al, 2013;and Jonard et al, 2015) or WRC measured by pressure plate extractors (e.g., Bittelli and Flury, 2009); multistep outflow (e.g., Bayer et al, 2005;Hollenbeck and Jensen, 1998;Neyshabouri et al, 2013;Weihermüller et al, 2009); or the evaporation (e.g., Schindler et al, 2010;Žydelis et al, 2018), mercury intrusion (e.g., Webb, 2001), and nitrogen sorption (e.g., Kowalczyk et al, 2003) methods. As an alternative, noninvasive measurement techniques can be used, such as MicroCT (e.g., Koestel, 2018;Pohlmeier et al, 2018;Smet et al, 2017), synchrotron radiation and/or microtomography (e.g., Peth et al, 2008), or nuclear magnetic resonance relaxometry (NMRR) (e.g., Jaeger et al, 2009;Stingaciu et al, 2010).…”