Using a combined global and local scaling 3D segmentation method to calculate pore connectivity measures in CT soil images

The study of the spatial characteristics of soil pore networks is essential to obtain different parameters that will be useful in developing simulation 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 network (pore space) from the CT soil images applying image binarization methods is a critical step. Different binarization methods can result in different spatial distributions of pores influencing the connectivity metrics used in the models.

A combined global & local 2D segmentation method called “Combining Singularity-CA method” was successfully applied improving pore space detection. 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 soil images. Combining Singularity-CA (2D) method obtains better performance than the Singularity-CA and the Maximum Entropy method applied individually to the soil images.

A new three dimensional binarization method is presented in this work. It combines the 3D Singularity-CV (Concentration Volume) method and a global one to improve 3D pore space detection. Porosity and connectivity metrics of soil pore spaces are calculated and compared to other segmentation methods.

 

Acknowledgements:

The authors acknowledge the support from Project No. PGC2018-093854-B-I00 of the "Ministerio de Ciencia, Innovación 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.