Automatic Crack Detection and Analysis for Biological Cellular Materials in X-Ray In Situ Tomography Measurements

Ziling Wu, Ting Yang, Zhifei Deng, Baokun Huang, Han Liu, Yu Wang, Yuan Chen, Mary Caswell Stoddard, Ling Li, Yunhui Zhu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)559-569
Number of pages11
JournalIntegrating Materials and Manufacturing Innovation
Issue number4
StatePublished - Dec 1 2019

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Industrial and Manufacturing Engineering


  • Cellular material
  • Computer vision
  • Crack detection
  • Machine learning
  • X-ray tomography


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