Application of Segmentation for Correction of Intensity Bias in X-Ray Computed Tomography Images

Abstract

Nondestructive imaging methods such as x-ray computed tomography (CT) yield high-resolution, grayscale, three-dimensional visualizations of pore structures and fluid interfaces in porous media. To separate solid and fluid phases for quantitative analysis and fluid dynamics modeling, segmentation is applied to convert grayscale CT volumes to discrete representations of media pore space. Unfortunately, x-ray CT is not free of artifacts, which complicates segmentation and quantitative image analysis due to obscuration of significant features or misinterpretation of attenuation values of a single material in different image sections. Images or volumes emanating from polychromatic (industrial) scanners are especially prone to high noise levels, beam hardening, scattered x-rays, or ring artifacts. These problems can be alleviated to a certain extent through application of metal filters, careful detector calibration, and sample centering, but they cannot be completely avoided. We have developed a simple three-dimensional approach to numerically correct for image artifacts using sequential segmentation. This procedure leads to a significant improvement of grayscale data as well as final segmentation results with reasonable computational demand.