Automatic Crack Detection and Analysis for Biological Cellular Materials in X-Ray In Situ Tomography Measurements
Source abstract
Abstract
Abstract 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.
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Research context
OpenAlex topics: Calcium Carbonate Crystallization and Inhibition · Bone Tissue Engineering Materials · Drilling and Well Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
Show source-provided references
1. Gibson L, Ashby M, Harly B (2010) Cellular materials in nature and medicine. Cambridge University Press, Cambridge
2. Gibson LJ, Ashby MF (1999) Cellular solids: structure and properties. Cambridge University Press, Cambridge
3. Albe M et al (2018) Interplay between calcite, amorphous calcium carbonate, and intracrystalline organics in sea urchin skeletal elements. Cryst Growth Des. https://doi.org/10.1021/acs.cgd.7b0162210.1021/acs.cgd.7b01622 ↗
4. Presser V, Schultheiß S, Berthold C, Nickel KG (2009) Sea Urchin Spines as a model-system for permeable, light-weight ceramics with graceful failure behavior. Part I. Mechanical behavior of Sea Urchin Spines under compression. J. Bionic Eng. 6:203–21310.1016/S1672-6529(08)60125-0 ↗
5. Seto J et al (2012) Structure-property relationships of a biological mesocrystal in the adult sea urchin spine. Proc Natl Acad Sci U. S. A. 109:3699–370410.1073/pnas.1109243109 ↗
6. Moureaux C et al (2010) Structure, composition and mechanical relations to function in sea urchin spine. J Struct Biol 170:41–4910.1016/j.jsb.2010.01.003 ↗
7. Yamada Y et al (2000) Compressive deformation behavior of Al2O3 foam. Mater Sci Eng A 277:213–21710.1016/S0921-5093(99)00541-9 ↗
8. Brezny R, Green DJ (1993) Uniaxial strength behavior of brittle cellular materials. J Am Ceram Soc 76:2185–219210.1111/j.1151-2916.1993.tb07753.x ↗
9. Tulliani JM, Montanaro L, Bell TJ, Swain MV (1999) Semiclosed-cell mullite foams: preparation and macro- and micromechanical characterization. J Am Ceram Soc 82:961–96810.1111/j.1151-2916.1999.tb01860.x ↗
10. Telford M (1985) Domes, arches and urchins: the skeletal architecture of echinoids (Echinodermata). Zoomorphology 105(2):114–12410.1007/BF00312146 ↗
11. Towe KM (1967) Echinoderm calcite: single crystal or polycrystalline aggregate. Science 157:1048–105010.1126/science.157.3792.1048 ↗
12. Dubois P, Ameye L (2001) Regeneration of spines and pedicellariae in echinoderms: a review. Microsc Res Tech 55:427–43710.1002/jemt.1188 ↗
13. Smith AB (1980) Stereom microstructure of the echinoid test. Spec Pap Palaeontology 25:1–81
14. Chen TT (2011) Microstructure and micromechanics of the sea urchin, Colobocentrotus atratus. Doctoral dissertation, Massachusetts Institute of Technology
15. Donnay G et al (1969) X-ray diffraction studies of echinoderm plates. Science 80(166):1147–115010.1126/science.166.3909.1147 ↗
16. Lowenstam HA, Weiner S (1989) On biomineralization. Oxford University Press, Oxford10.1093/oso/9780195049770.001.0001 ↗
17. Weber JN (1969) The incorporation of magnesium into the skeletal calcites of echinoderms. Am J Sci 267:537–56610.2475/ajs.267.5.537 ↗
18. Kim E et al (2016) Suppressed instability of a-igzo thin-film transistors under negative bias illumination stress using the distributed bragg reflectors. IEEE Trans Electron Devices 63:1066–107110.1109/TED.2015.2513414 ↗
19. Cheng HD, Shi XJ, Glazier C (2003) Real-time image thresholding based on sample space reduction and interpolation approach. J Comput Civ Eng 17:264–27210.1061/(ASCE)0887-3801(2003)17:4(264) ↗
20. Ying L, Salari E (2010) Beamlet transform-based technique for pavement crack detection and classification. Comput Civ Infrastruct Eng 25:572–58010.1111/j.1467-8667.2010.00674.x ↗
21. Santhi B, Krishnamurthy G, Siddharth S, Ramakrishnan PK (2012) Automatic detection of cracks in pavements using edge detection operator. J Theor Appl Inf Technol 36:199–205
22. Nisanth A, Mathew A (2014) Automated visual inspection of pavement crack detection and characterization. Int J Technol Eng Syst 6:14–20
23. Xie X, Xia Y, Liu B, Li K, Wang T (2019) The multichannel integration active contour framework for crack detection. Int J Adv Robot Syst 16:1–13
24. Sinha SK, Karray F (2002) Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm. IEEE Trans Neural Netw 13:393–40110.1109/72.991425 ↗
25. Lei H, Cheng J, Xu Q (2019) Cement pavement surface crack detection based on image processing. Mech Eng Sci 1:63–7010.1177/0954406218756939 ↗
26. Li G, Zhao X, Du K, Ru F, Zhang Y (2017) Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine. Autom Constr 78:51–6110.1016/j.autcon.2017.01.019 ↗
27. Cavalin P, Oliveira LS, Koerich AL, Britto AS (2006) Wood defect detection using grayscale images and an optimized feature set. In: IECON conference on industrial electronics. pp 3408–3412. https://doi.org/10.1109/iecon.2006.34761810.1109/iecon.2006.347618 ↗
28. Gu IYH, Andersson H, Vicen R (2010) Wood defect classification based on image analysis and support vector machines. Wood Sci Technol 44:693–70410.1007/s00226-009-0287-9 ↗
29. Kong X, Li J (2018) Vision-based fatigue crack detection of steel structures using video feature tracking. Comput Civ Infrastruct Eng 33:783–79910.1111/mice.12353 ↗
30. Wang J. et al. (2016) CNN–RNN: a unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2285–2294 Dec 2016
31. Wu Z, Yang T, Li L, Zhu Y (2019) A hierarchical reconstruction of x-ray phase tomography based on transferred non-local structural features. https://doi.org/10.1117/12.251905510.1117/12.2519055 ↗
32. Wu Z, Yang T, Li L, Zhu Y (2019) Hierarchical convolutional network for sparse-view X-ray CT reconstruction. https://doi.org/10.1117/12.252123910.1117/12.2521239 ↗
33. Wu, Z., Alorf, A., Yang, T., Li, L. & Zhu, Y. Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks. 1–9
34. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–144810.1109/ICCV.2015.169 ↗
35. Jiang H, Learned-Miller E (2017) Face detection with the faster R-CNN. In: Proc 12th IEEE Conf Autom Face Gesture Recognition, pp 650–657. https://doi.org/10.1109/FG.2017.8210.1109/FG.2017.82 ↗
36. Sun X, Wu P, Hoi SCH (2018) Face detection using deep learning: an improved faster RCNN approach. Neurocomputing 299:42–5010.1016/j.neucom.2018.03.030 ↗
37. Li X, Shang M, Qin H, Chen L (2015) Fast accurate fish detection and recognition of underwater images with Fast R-CNN. In: Ocean 2015-MTS/IEEE Washington, 1–5 2017. https://doi.org/10.23919/oceans.2015.740446410.23919/oceans.2015.7404464 ↗
38. Hoang Ngan Le T, Zheng Y, Zhu C, Luu K, Savvides M (2016) Multiple scale faster-RCNN approach to driver’s cell-phone usage and hands on steering wheel detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 46–53, 2016. https://doi.org/10.1109/cvprw.2016.1310.1109/cvprw.2016.13 ↗
39. Braun M, Rao Q, Wang Y, Flohr F (2016) Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. In: IEEE conference on intelligent transportation systems, ITSC. https://doi.org/10.1109/itsc.2016.779576310.1109/itsc.2016.7795763 ↗
40. Kundu A, Li Y, Rehg JM (2018) 3D-RCNN: instance-level 3D object reconstruction via render-and-compare. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 3559–3568. https://doi.org/10.1109/cvpr.2018.0037510.1109/cvpr.2018.00375 ↗
41. Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Civ Infrastruct Eng 32:361–37810.1111/mice.12263 ↗
42. Zhang L, Yang F, Daniel Zhang Y, Zhu YJ (2016) Road crack detection using deep convolutional neural network. In: Proceedings of international conference on image processing, ICIP, 3708–3712 Aug 201610.1109/ICIP.2016.7533052 ↗
43. Li W, Field KG, Morgan D (2018) Automated defect analysis in electron microscopic images. npj Comput Mater 4:1–910.1038/s41524-017-0060-9 ↗
44. Gürsoy D, De Carlo F, Xiao X, Jacobsen C (2014) TomoPy: a framework for the analysis of synchrotron tomographic data. J Synchrotron Radiat 21:1188–119310.1107/S1600577514013939 ↗
45. Presser V et al (2009) Sea Urchin Spines as a model-system for permeable, light-weight ceramics with graceful failure behavior. Part II. Mechanical behavior of sea urchin spine inspired porous aluminum oxide ceramics under compression. J Bionic Eng 6:357–36410.1016/S1672-6529(08)60143-2 ↗