3D Microstructure Segmentation by Topological Persistence

Tomography is a widely used tool for analyzing microstructures in three dimensions (3D). The analysis, however, faces difficulty because the constituent materials produce similar grey-scale values. Sometimes, this prompts the image segmentation process to assign a pixel/voxel to the wrong phase (active material or pore). Consequently, errors are introduced in the microstructure characteristics calculation. In this work we develop a filtering algorithm based on topological persistence, a technique used in topological data analysis. One problem faced when evaluating filtering algorithms is that real image data in general are not equipped with the ground truth information about the microstructure characteristics. For this study, we construct synthetic images for which the ground truth values are known. Specifically, we compare interconnected pore tortuosity and phase fraction. Experimental results show that our filtering algorithm provides a significant improvement in reproducing tortuosity close to the ground truth, even when the grey-scale values of the phases are similar.