Topological Filtering for 3D Microstructure Segmentation

TL;DR

This paper introduces PerSplat, a topological filtering algorithm that enhances 3D microstructure segmentation by reducing errors caused by similar grey-scale values, validated on synthetic and real images.

Contribution

The study presents a novel topological filtering method, PerSplat, which improves segmentation accuracy in 3D microstructure analysis over existing techniques.

Findings

PerSplat outperforms TV and NL-means in synthetic image segmentation.

PerSplat significantly improves segmentation quality in real 3D microstructure images.

Synthetic data with known ground truth enables quantitative comparison of filtering methods.

Abstract

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 called PerSplat based on topological persistence (a technique used in topological data analysis) to improve segmentation quality. One problem faced when evaluating filtering algorithms is that real image data in general are not equipped with the `ground truth' for the microstructure characteristics. For this study, we construct synthetic images for which the ground-truth values are known. On the synthetic images, we compare the pore tortuosity and Minkowski functionals (volume and surface area) computed with our PerSplat filter and other methods such as total variation (TV) and non-local means (NL-means). Moreover, on a real 3D image, we visually compare the segmentation results provided by our filter against TV and NL-means. The experimental results indicate that PerSplat provides a significant improvement in segmentation quality.