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.

翻译：无损检测方法对于建立多孔材料的微观结构几何特征与其力学行为之间的联系至关重要，因为受限于材料可用性或不可重复的实验条件，破坏性测试往往难以实施。显微计算机断层扫描（micro-CT）能够提供多孔微观结构的高分辨率三维重建，从而实现对几何描述符的直接量化。形态计量学理论的最新进展表明，需要四个独立的形态计量指标（孔隙率、表面积、平均曲率和欧拉特征）才能完整捕捉微观结构与强度之间的关系，由此构成了广义强度定律的基础。为促进该框架的实际应用，本研究开发了一款Fiji插件，能够自动从显微CT数据集中提取这四个形态计量指标（孔隙率、表面积、平均曲率、欧拉特征）。该插件集成于Fiji平台，提供可重复、易获取且用户友好的分析功能。工具应用实例表明，提取的描述符可便捷地融入本构模型与机器学习工作流，实现应力-应变行为的正向预测以及微观结构的逆向设计。该方法支持无损评估，加速材料筛选进程，并推动多孔介质研究中成像技术与预测建模的深度融合。