Pore detection in 3-D CT soil samples through an improved sub-segmentation method

Abstract

X-ray computer tomography (CT) is a non-invasive technique for image acquisition. Recent technologicaladvances have enabled reliable and high-resolution images to be obtained. In soil samples, for example, thissubserves the identication of pores and their structure and the analysis of their geometric characteristics.However, the lack of contrast between pores and solids in soil samples makes it difcult to identify the pores, andit poses problems for their connectivity when a three-dimensional (3-D) reconstruction is made from a group ofconsecutive 2-D images obtained with a scanner. To solve this problem, an improved sub-segmentation method,which had been developed and tested previously, was applied in this research to achieve a better identication ofthe pore space and consequently the solid space in the 2-D slices of the image, followed by a 3-D reconstructionof the soil sample. In this study, two soil samples were used, one real soil sample with 255 2-D CT consecutiveimages and a synthetic image with 215 2-D images. The latter sample was used only to evaluate the robustnessof the improved sub-segmentation method and the results from analysis of the pore connectivity in a knownstructure. The results obtained with the improved sub-segmentation were compared with those of traditionalclustering algorithms for image segmentation by k-means, fuzzy c-means and Otsu’s methods. The results werepromising, and the 3-D reconstruction presents a realistic structure for the continuity and coincidence of theshapes of the pores in the consecutive images. In addition, the pore regions detected have a small non-uniformity(NU) value, which indicates both good pore detection and homogeneity, which facilitates pore connectivitybetween the different 2-D images.