数字式射线图像缺陷检测的C-V方法

C-V method of defects detection in digital radiography image

数字式射线图像(DR图像)缺陷检测主要是进行缺陷区域的分割和测量,分割精度将直接影响到测量精度。C-V模型是一种新的基于曲线演化理论和水平集方法的图像分割模型,它结合区域信息使得分割结果全局最优,可以很自然地处理轮廓线拓扑结构的变化。针对工件DR图像特点,研究了一种DR图像缺陷检测的C-V方法:首先应用C-V模型进行DR图像缺陷区域的分割,在此基础上,完成缺陷区域几何参数的测量。实验表明,C-V方法能准确地分割出DR图像中的缺陷区域,并获得缺陷形心和面积等参数。

The primary work of defect detection of Digital Radiography（DR） image is the segmentation and measurement of defect regions.The precision of defect measurement,to a large extent,depends on the accuracy of segmentation.Based on the curve evolution theory and level set method for segmentation,C-V model has taken regional information into account so that a globally optimal solution can be got,on the other hand,this model also can naturally handle contour topology changes.Aiming at the traits of DR image,this paper researches on a C-V method of defects detection in DR image.In the method, firstly,C-V model is applied to defects segmentation for DR image,on this basis,defects measurement is achieved.The experiment shows that the C-V method can accurately divide defect regions of DR image,and get its geometric parameters.