Virtual characterisation of porcupine quills using X-ray micro-CT

Morphological properties and structures of the porcupine quills of different sizes can be explored in detail using micro-CT scans. Information including the porosity, volume thickness, number and lengths of stiffeners within quills are extracted via two modalities, using commercially available software and custom-developed scripts. Three segmentation methods, including the Otsu global thresholding, histographic segmentation and deep learning segmentation, are applied to identify the porosity variation based on different segmentation methods. Over segmentation is found when applying Otsu global thresholding, yielded the highest porosity. Histographic segmentation performed better than Otsu thresholding in segmenting CT slices, however speckles are observed in foam areas with lower intensity. Among all methods, deep learning segmentation resulted in the most reliable segmentation, and showed a consistent porosity across different quill sizes. Obtained results provide essential information for biomimicry research with an aim towards designing stronger and lighter structures for various engineering applications.

Automated summary

The morphological properties and structures of porcupine quills of different sizes were analyzed using micro-CT scans. Various segmentation methods were applied to identify the porosity variation, with deep learning segmentation proving to be the most reliable. The obtained results provide important information for biomimicry research and engineering applications.