Automatic Crack Detection and Analysis for Biological Cellular Materials in X-Ray In Situ Tomography Measurements
Automatic Crack Detection and Analysis for Biological Cellular Materials in X-Ray In Situ Tomography Measurements
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DOI：
10.1007/s40192-019-00162-3
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发表时间：
2019-11
影响因子：
3.3
通讯作者：
Ziling Wu;Ting Yang;Zhifei Deng;Baokun Huang;Han Liu;Yu Wang;Yuan Chen;M. Stoddard;Ling Li;Yunhui Zhu
中科院分区：
文献类型：
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作者：
Ziling Wu;Ting Yang;Zhifei Deng;Baokun Huang;Han Liu;Yu Wang;Yuan Chen;M. Stoddard;Ling Li;Yunhui Zhu
我们介绍了一种新的方法，基于原位X射线层析测量，来定量和分析生物细胞材料在损伤过程中的三维裂纹形态。由于很难从复杂的三维网络结构中识别和记录裂纹，所以多孔材料的损伤表征是具有挑战性的。在本文中，我们开发了一系列计算机视觉算法，以从压缩试验期间获得的大量原位X射线层析成像测量数据集中提取裂纹模式。基于基于模型的特征过滤和数据驱动的机器学习的混合方法，该方法在识别复杂的细胞结构和层析重建伪影中的裂纹模式方面表现出了高效率和高精度。识别出的裂纹被登记为3D倾斜平面，其中包括裂纹位置、裂纹张开宽度和裂纹面方向的3D形态描述符被登记，以便为未来的力学分析提供定量数据。该方法被应用于两种不同孔隙度的生物材料，即海胆(Heteroctrotus Mamiillatus)的刺和贻贝(Dromaius Novaehollandiae)的蛋壳。这一结果得到了经验丰富的人体图像阅读器的验证。本文提出的方法可用于许多其他多孔固体的裂纹分析，包括合成材料和天然材料。
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.