摘要
工业CT裂纹分割是工业CT图像处理中的一项关键技术,而CT图像中的伪影、噪声等干扰会对裂纹分割带来极大的困扰。为了提高CT图像中裂纹的分割精度,本文通过分析CT图像中裂纹的特征,提出了结合Hessian矩阵和支持向量机的CT图像裂纹识别与分割的方法。首先采用基于Hessian矩阵的多尺度滤波方法提取CT图像中的线状结构并对提取的线状结构进行对比度增强;然后利用图像像素点灰度在空间的分布规律提取线状结构图像的纹理特征信息;再使用基于径向基函数的支持向量机训练裂纹识别分类器;进而使用训练好的模型定位测试图像中裂纹所在的方块区域;最后使用自动阈值分割算法得到CT图像中的裂纹。实验结果表明,结合Hessian矩阵和支持向量机的分割方法能够抑制图像中非目标区域,提高了算法的抗干扰性,对裂纹子图像识别的准确率可达94.5%,具有实际的工程应用价值。
Crack segmentation plays an important role in industrial CT image processing.However,interference in CT images,such as noise and artifacts,can adversely affect the accuracy and precision of crack segmentation.To improve crack segmentation precision in CT image processing,this paper analyzes the characteristics of cracks in CT images,and proposes a method for CT image crack recognition and segmentation that combines a Hessian matrix with a support vector machine.Firstly,a linear filter based on a Hessian matrix is used to extract the linear structures from a CT image and enhance the contrast of these linear structures.Moreover,to represent the texture features of these linear structure images,the method directly extracts textural feature information using a Grey Level Co-occurrence Matrix,which reflects the spatial distribution of grayscale.In addition,a crack identification classifier is trained by a Support Vector Machine(SVM),which is based on a Radial Basis Function(RBF)kernel.Furthermore,the crack identification classifier is used to locate the block area positions of cracks in CT images.Finally,the binary segmentation results for cracks are obtained by Otsu threshold segmentation.The experiments demonstrate that this proposed method can improve the anti-jamming resistance of the algorithm by shielding the non-interest region in the image,and the recognition accuracy reaches 94.5%.This algorithm has practical engineering application value as it has high recognition accuracy and high segmentation accuracy.
作者
邹永宁
张智斌
李琦
余浩松
ZOU Yong-ning;ZHANG Zhi-bin;LI Qi;YU Hao-song(College of Optoelectronic Engineering,Chongqing University,Chongqing 400044,China;ICT Research Center,Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China,Chongqing University,Chongqing 400044,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2021年第10期2517-2527,共11页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.11827809)。
