Deep learning-based hybrid reconstruction algorithm for fibre instance segmentation from 3D X-ray tomographic images
Chemical Journal of Chemical Engineering,
Mengqi Fang, Aurélien Sibellas, James Drummond, Yankai Cao, Andre Phillion, Mark Martinez, Vijay Kumar Pediredla, Bhushan Gopaluni
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3D x-ray tomography is a powerful scanning technique used for generating images of complex fibre structures. A novel machine-learning algorithm to identify and separate individual fibres using 3D images is proposed in this article. The developed four-step hybrid 3D fibre segmentation algorithm involves deep-learning aided semantic segmentation that slices 3D images to create 2D images for fibre extraction, elliptical contour estimation combined with the marker-controlled watershed algorithm for separating fibres from the background area, identifying individual fibres through 3D reconstruction, and, lastly, the 3D object refining approach based on outlier object detection and replacement. The proposed methodology is implemented on a real-time sample of nylon fibre bundle under compression and its 3D x-ray image volume to validate the performance. The results show its superior performance compared to off-the-shelf image processing algorithms in terms of precision, that is, with a validation accuracy greater than 90%, and efficiency, that is, preventing the need for a huge data set and reducing the complexity.
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