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.

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To describe various important soil processes like the release of greenhouse gases or the proliferation of microorganisms, it is necessary to assess quantitatively how the geometry and in particular the connectivity of the air-filled pore space of a soil evolves as it is progressively dried. The availability of X-ray computed microtomography (μCT) images of soil samples now allows this information to be obtained directly, without having to rely on the interpretation of macroscopic measurements using capillary theory, as used to be the case. In this general context, we present different methods to describe quantitatively the configuration of the air-filled pore space in 3D μCT images of 20 separate samples of a loamy soil equilibrated at different matric potentials. Even though measures using μCT on such multi-scale materials strongly depend on image resolution, our results show that in general, soil samples most often behave as expected, for example, connectivity increases with higher negative matric potential, while tortuosity decreases. However, simple correlations could not be found between the evolution of quantitative descriptors of the pore space at the different matric potentials and routinely measured macroscopic soil parameters. A statistical analysis of all soil samples concurrently confirmed this lack of correspondence.