基于空间直觉模糊c均值的三维CT图像缺陷体积测量算法

Li-ze Zhang, Kuan Shen
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引用次数: 0

摘要

三维计算机断层扫描(CT)图像的缺陷体积测量在缺陷分析中具有重要意义。目前针对缺陷的体积测量算法很少,难以满足实际应用的要求。为了解决这一问题,提出了一种自动体积测量算法,以更精确地获得三维CT图像中的缺陷体积。利用空间直觉模糊c均值(SIFCM)对每个切片图像中的缺陷进行分割。与模糊c均值(FCM)和OTSU算法相比,缺陷分割具有更好的效果和准确性。随后,利用分割后的二值图像中生长的三维区域计算所连接部件的缺陷体积。实验结果证明了该算法的可行性和有效性,特别是对于较大缺陷的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Volumetric Measurement Algorithm of Defects in 3D CT Image Based on Spatial Intuitionistic Fuzzy C-means
Defect volume measurement of 3D computed tomography (CT) image is of great importance in defect analysis. Currently, there are few volumetric measurement algorithms for defects, and it is difficult to meet the requirements of practical applications. To address this issue, an automatic volumetric measurement algorithm is proposed to obtain the defect volume in a 3D CT image in a more precise way. Spatial intuitionistic fuzzy C-means (SIFCM) is used to segment the defects in each slice image. Compared with fuzzy C-means (FCM) and OTSU algorithms, defect segmentation can achieve better results and accuracy. Subsequently, the defect volume of the connected component can be calculated using the 3D region growing in the segmented binary images. The experimental results demonstrate the feasibility and effectiveness of the proposed algorithm, especially for large defects.
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