StrengthLawExtractor: A Fiji plugin for 3D morphological feature extraction from X-ray micro-CT data

TL;DR

StrengthLawExtractor is a Fiji plugin that automates the extraction of key 3D morphometric features from micro-CT data, facilitating non-destructive analysis and modeling of porous materials' strength.

Contribution

The paper introduces a novel Fiji plugin that automatically computes four critical morphometric measures from micro-CT datasets, enhancing practical application of strength laws.

Findings

Enables automated extraction of porosity, surface area, mean curvature, and Euler characteristic.

Supports integration with constitutive models and machine learning workflows.

Facilitates non-destructive evaluation and microstructure design.

Abstract

Non-destructive methods are essential for linking the microstructural geometry of porous materials to their mechanical behavior, as destructive testing is often infeasible due to limited material availability or irreproducible conditions. Micro-computed tomography (micro-CT) provides high resolution three dimensional reconstructions of porous microstructures, enabling direct quantification of geometric descriptors. Recent advances in morphometric theory have demonstrated that four independent morphometric measures (porosity, surface area, mean curvature, and Euler characteristic) are required to capture the relationship between microstructure and strength, thereby forming the basis of generalized strength laws. To facilitate practical application of this framework, a Fiji plugin was developed to extract the four morphometric measures (porosity, surface area, mean curvature, Euler characteristic) from micro-CT datasets automatically. The plugin integrates within the Fiji platform to provide reproducible, accessible, and user friendly analysis. The application of the tool demonstrates that the extracted descriptors can be readily incorporated into constitutive models and machine learning workflows, enabling the forward prediction of stress-strain behavior as well as the inverse design of microstructures. This approach supports non-destructive evaluation, accelerates materials selection, and advances the integration of imaging with predictive modeling in porous media research.