工业CT图像圆精确测量  

Precise measurement of circles in industrial computed tomographic images

作　　者：刘丰林[1] 乔桂锋[1] 邹斌[1] 

机构地区：[1]重庆大学光电技术与系统教育部重点实验室ICT研究中心,重庆400030

出　　处：《光学精密工程》2009年第11期2842-2848,共7页Optics and Precision Engineering

基　　金：国家863高技术研究发展计划资助项目(No.2006AA04Z104)

摘　　要：为了自动检测工业CT图像中的圆并精确地测量其参数,研究了基于Facet模型的亚像素边缘检测算法检测图像边缘,利用圆存在的概率大小识别边缘图像中的圆并测量其参数的方法。首先,利用Facet模型提取图像的亚像素边缘。接着,研究了图像圆存在概率算法特点,通过构造专用链表数据结构存储计算数据,限制计算时圆心选取范围等方法,克服了原计算方法效率低、占用内存大的缺点,并运用改进的圆存在概率计算方法对工业CT图像中的圆进行自动检测。最后,通过最小二乘法拟合圆边缘点,计算圆参数的实际尺寸。使用空间分辨力为2.0lp/mm的电子直线加速器工业CT系统,扫描重建出包含10个圆的800pixel×800pixel工业CT图像,运用该方法对图像中的圆进行了识别和测量。试验结果表明,改进的圆存在概率算法计算效率明显提高,图像中圆参数测量精度<0.5%,满足工业CT图像圆自动测量的精确、快速、可靠等要求。In order to detect circles automatically and to obtain the parameters in images precisely, a method to measure circles in industrial CT images was developed. Firstly, the sub-pixel edge of an image was detected based on a Facet model. Then, a method for calculating the existence probability map of circles and its defects were analyzed. To overcome the defects of long-time calculation and memory consumption, the present method made sure the potential range of the center of a circle and the stored message, which met the required numerical values in a chain. Finally, the circles were detected based on the improved method, and the circle parameters were calculated with Least Square Algorithm（LSA）. The method was applied to measure an image with 10 circles obtained from an industrial CT system with the spatial resolution of 2.0 lp/mm. The results show that this method has greatly improved the calculation rate, and the measurement accuracy for circles is better than 0.5%. It can satisfy the measurement requirements of the circles for the higher speed, higher precision and non-contact.

关 键 词：FACET模型 存在概率 计算机层析成像 图像测量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]