Unsupervised 3-D Multiphase Segmentation of X-Ray CT Data of Porous Materials.

Technical advancements of noninvasive imaging methods such as X-Ray Computed Tomography (CT) led to a recent surge of applications in Earth Sciences. While substantial efforts and resources have been devoted to advance CT technology and micro-scale analysis, the development of a stable 3-D multiphase image segmentation technique applicable to large datasets is lacking. To eliminate the need for wet/dry or dual energy scans, image alignment, and subtraction analysis, commonly applied in synchrotron X-Ray micro CT, an unsupervised  segmentation method based on a Bayesian Markov Random Field (MRF) framework amenable to true 3-D multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for natural and artificial porous media datasets demonstrate great potential of the MRF image model for 3-D multiphase segmentation.