基于改进 SIFCM 和区域生长的三维 CT 图像缺陷体积测量
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TP391

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国家自然科学基金(52075057)、科技部重点研发计划(2022YFF0706402)项目资助


Defect volume measurement in 3D CT image based on improved SIFCM and region growing
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    摘要:

    针对工件三维 CT 图像中孔洞和空腔缺陷体积测量问题,本文提出了一种基于改进型空间直觉模糊 C 均值聚类(NLSIFCM)和三维区域生长的内部缺陷体积自动测量算法。 首先对采集得到三维 CT 图像进行预处理;随后使用 NL-SIFCM 在三 维 CT 图像上分割得到二值化缺陷图像组,同时针对三维 CT 图像切片间具有空间相似性改进得到快速算法;最后对二值化图 像组进行三维区域生长得到缺陷体素数和空间结构,并将缺陷空间结构显示于三维可视化软件中辅助检测人员分析缺陷。 实 验结果表明,对用于模拟缺陷的标准球体积测量值相对误差在 1. 0%以内,具有较高测量精度;并通过实际工件检测验证了该算 法适用性可有效满足 CT 检测需求。

    Abstract:

    This paper proposed an internal defect volume automatic measurement algorithm based on the improved spatial intuitionistic fuzzy C-means clustering (NL-SIFCM) and 3D region growing for measuring the volume of holes and cavities in 3D CT images of workpieces. Firstly, the acquired 3D CT images are pre-processed. Subsequently, NL-SIFCM was used to segment on 3D CT image to obtain a binarized defect image set, while a fast algorithm was obtained for 3D CT image having spatial similarity between slices. Finally, the binary defect image set for 3D region growing to obtain the defect voxel number and spatial structure, and the defect spatial structure is displayed in the 3D visualization software to assist inspectors in analyzing defects. The experimental results show that the measured volume of the standard spherical volume used to simulate defects has a relative error of less than 1. 0%, indicating a high level of measurement accuracy. The applicability of the algorithm has been validated by actual workpiece inspection, demonstrating its effectiveness in meeting the demands of CT inspection.

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张理泽,沈 宽,张 博.基于改进 SIFCM 和区域生长的三维 CT 图像缺陷体积测量[J].电子测量与仪器学报,2023,37(6):77-85

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  • 在线发布日期: 2023-09-22
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