Combining global and local scaling (3D) methods to detect pore spaces in CT soil images

&amp;lt;p&amp;gt;The characterization of the spatial distribution of soil pore structures is essential to obtain different parameters that will be useful in developing predictive models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore structure (pore space) from the CT soil images applying image segmentation methods is crucial when attempting to extract complex pore space geometry information.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Different segmentation methods can result in different spatial distributions of pores influencing the parameters used in the models [1]. A new combined global &amp;amp; local segmentation (2D) method called &amp;amp;#8220;Combining Singularity-CA method&amp;amp;#8221; was successfully applied [2]. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT images [3]. Comparing Singularity-CA method with classical methods, such as Otsu and Maximum Entropy, we observed that more pores can be detected mainly due to its ability to amplify anomalous concentrations. However, some small pores were detected incorrectly. Combining Singularity-CA (2D) method gives better pore detection performance than the Singularity-CA and the Maximum Entropy method applied individually to the images.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Combining Singularity-CV (3D) method is presented in this work. It combines the Singularity &amp;amp;#8211; CV (Concentration Volume) method [4] and a global one to improve 3D pore space detection.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;References:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1] Zhang, Y.J. (2001). A review of recent evaluation methods for image segmentation: International symposium on signal processing and its applications. Kuala Lumpur, Malaysia, 13&amp;amp;#8211;16, pp. 148&amp;amp;#8211;151.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[2] Mart&amp;amp;#237;n-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Paz-Gonz&amp;amp;#225;lez, A., and Tarquis, A.M. (2018). Combining global and local scaling methods to detect soil pore space. J. of Geo. Exploration, vol. 189, June 2018, pp 72-84.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[3] Mart&amp;amp;#237;n-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2017). New segmentation method based on fractal properties using singularity maps. Geoderma, vol. 287, February 2017, pp 40-53. http://dx.doi.org/10.1016/j.geoderma.2016.09.005.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[4] Mart&amp;amp;#237;n-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2018). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, vol. 311, February 2018, pp 175-188. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Acknowledgements:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovaci&amp;amp;#243;n y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.&amp;lt;/p&amp;gt;