In the study of the composite materials performance, X-ray computed tomography (XCT) scanning has always been one of the important measures to detect the internal structures. CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process, which is of great significance to the study of material performance. This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images. In the core layer area, area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm. In the transition layer area, a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result. Furthermore, a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed. It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials, and the accuracy of segmentation is significantly improved in comparison with the original method, which ensures the accuracy and robustness of the subsequent fiber feature extraction process
XUE Yongboa (薛永波)，LIU Zhaob (刘钊)， LI Zeyanga (李泽阳)，ZHU Pinga* (朱平)
. CT Image Segmentation Method of Composite Material Based on Improved Watershed Algorithm and U-Net Neural Network Model[J]. Journal of Shanghai Jiaotong University(Science), 2023
, 28(6)
: 783
-792
.
DOI: 10.1007/s12204-021-2385-2
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