Statistical segmentation and porosity quantification of 3D X-ray micro-tomography

High-resolution x-ray micro-tomography is used for imaging of solid materials at micrometer scale in 3D. Our goal is to implement nondestructive techniques to quantify properties in the interior of solid objects, including information on their 3D geometries, which supports modeling of the fluid dynamics into the pore space of the host object. The micro-tomography data acquisition process generates large data sets that are often difficult to handle with adequate performance when using current standard computing and image processing algorithms. We propose an efficient set of algorithms to filter, segment and extract features from stacks of image slices of porous media. The first step tunes scale parameters to the filtering algorithm, then it reduces artifacts using a fast anisotropic filter applied to the image stack, which smoothes homogeneous regions while preserving borders. Next, the volume is partitioned using statistical region merging, exploiting the intensity similarities of each segment. Finally, we calculate the porosity of the material based on the solid-void ratio. Our contribution is to design a pipeline tailored to deal with large data-files, including a scheme for the user to input image patches for tuning parameters to the datasets. We illustrate our methodology using more than 2,000 micro-tomography image slices from 4 different porous materials, acquired using high-resolution X-ray. Also, we compare our results with standard, yet fast algorithms often used for image segmentation, which includes median filtering and thresholding.