J Shanghai Jiaotong Univ Sci ›› 2023, Vol. 28 ›› Issue (6): 783-792.doi: 10.1007/s12204-021-2385-2
薛永波a,刘 钊b,李泽阳a,朱 平a
接受日期:
2020-07-14
出版日期:
2023-11-28
发布日期:
2023-12-04
XUE Yongboa (薛永波),LIU Zhaob (刘钊), LI Zeyanga (李泽阳),ZHU Pinga* (朱平)
Accepted:
2020-07-14
Online:
2023-11-28
Published:
2023-12-04
摘要: 在复合材料性能的研究中,XCT扫描一直是检测其内部结构特点的重要手段之一,利用CT图像分割技术将显著提高后续材料特征提取精度,对材料性能研究有重要意义。本研究针对复合材料CT图像中纤维截面粘连导致的图像分割准确率低的问题,在芯层区域通过形态学指标评估区域有效性并基于分水岭算法提出迭代分割策略;在过渡层区域利用人工标记训练U-net神经网络模型并应用于分割结果预测,进而提出一种基于改进分水岭算法结合U-net模型的纤维复合材料CT图像分割方法。经实验验证,本方法对复合材料CT图像分割问题具有良好的适应性和有效性,相较于未改进方法分割准确率得到显著提升,保证了后续纤维特征提取过程的准确率和鲁棒性。
中图分类号:
薛永波a,刘 钊b,李泽阳a,朱 平a. 基于改进分水岭算法和U-net神经网络模型的复合材料CT图像分割方法[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 783-792.
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]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 783-792.
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