Non-destructive methods are essential for linking the microstructural geometry of porous geo-materials to their mechanical behavior, as destructive testing is often infeasible due to limited material availability or irreproducible conditions. X-ray 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 from X-ray micro-CT datasets automatically. The plugin integrates within the Fiji platform to provide reproducible, accessible, and user friendly analysis. The extracted descriptors can be readily incorporated into constitutive models and machine learning workflows for forward prediction of stress–strain behavior and inverse microstructure design. This approach supports non-destructive evaluation, accelerates materials selection, and advances the integration of imaging with predictive modeling in porous media research.
