We introduce a novel methodology, based on in situ X-ray tomography measurements, to quantify and analyze 3D crack morphologies in biological cellular materials during damage process. Damage characterization in cellular materials is challenging due to the difficulty of identifying and registering cracks from the complicated 3D network structure. In this paper, we develop a pipeline of computer vision algorithms to extract crack patterns from a large volumetric dataset of in situ X-ray tomography measurement obtained during a compression test. Based on a hybrid approach using both model-based feature filtering and data-driven machine learning, the proposed method shows high efficiency and accuracy in identifying the crack pattern from the complex cellular structures and tomography reconstruction artifacts. The identified cracks are registered as 3D tilted planes, where 3D morphology descriptors including crack location, crack opening width, and crack plane orientation are registered to provide quantitative data for future mechanical analysis. This method is applied to two different biological materials with different levels of porosity, i.e., sea urchin (Heterocentrotus mamillatus) spines and emu (Dromaius novaehollandiae) eggshells. The results are verified by experienced human image readers. The methodology presented in this paper can be utilized for crack analysis in many other cellular solids, including both synthetic and natural materials.

中文翻译：

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X射线原位层析测量中生物细胞材料的自动裂纹检测和分析

我们引入一种基于原位X射线断层扫描测量的新颖方法，以量化和分析损伤过程中生物细胞材料中的3D裂纹形态。由于难以从复杂的3D网络结构中识别和记录裂缝，因此蜂窝材料中的损伤表征具有挑战性。在本文中，我们开发了一系列计算机视觉算法，以从压缩测试期间获得的大量原位X射线断层扫描测量数据集中提取裂纹图案。基于使用基于模型的特征过滤和数据驱动的机器学习的混合方法，该方法在从复杂的细胞结构和层析成像重建伪像中识别裂纹模式方面显示出高效率和准确性。识别出的裂纹被记录为3D倾斜平面，其中记录了包括裂纹位置，裂纹开口宽度和裂纹平面方向的3D形态描述子，以提供定量数据以供将来进行机械分析。该方法适用于孔隙率不同的两种不同生物材料，即海胆（Heterocentrotus mamillatus）刺和e（Dromaius novaehollandiae）蛋壳。结果由经验丰富的人类图像阅读器验证。本文介绍的方法可用于许多其他多孔固体（包括合成材料和天然材料）的裂纹分析。