基于灰度变化率的低对比度CT图像分割研究  

A Defect Segmentation Algorithm for Low Contrast CT Images Based on the Change Rate of Gray Level

作　　者：时佳悦 张蕊萍[1] 董海鹰[1,2] 苟军年[1] 

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070 [2]兰州交通大学新能源与动力工程学院,甘肃兰州733000

出　　处：《兰州交通大学学报》2017年第3期57-62,共6页Journal of Lanzhou Jiaotong University

基　　金：兰州市科技计划项目(2014-2-7)

摘　　要：整幅工业CT图像具有部分区域对比度低,灰度范围狭窄,灰度变化不明显等特点,针对传统的缺陷分割方法无法对低对比度区域的缺陷进行精确分割的问题,提出一种基于灰度变化率的缺陷分割方法.通过提取图像中某点的灰度值并计算该点与其周围邻域内点的平均灰度值的变化率、差值以及方差,选取符合图像分割条件范围内的点作为边界点,从而提取工业CT图像中低对比度区域缺陷的边界并进行分割.仿真实验表明,本文方法分割CT图像的缺陷准确率可达到95%,能够快速确定缺陷区域,并准确、有效地分割提取缺陷.According to the characteristics of low contrast, narrow gray range and inconspicuous change of gray value in the part of industrial CT image, and the problem of difficult defect seg-mentation of low contrast region of the traditional segmentation method, a defect segmentation method is proposed based on the change rate of gray level. In order to determine and split the edge of defects in low contrast,the change rate of average gray value, difference and variance between the point and the neighborhood point are calculated, respectively. The points which accord with the condition of image segmentation are selected as boundary points, and the defect edge of low contrast area is identified and segmented in industrial CT image. Simulation results show that the proposed algorithm can increase the accuracy rate of segmentation to 95 % ,can quickly determine the defect area,and accurately and effectively segment and extract the defects.

关 键 词：低对比度 缺陷分割 灰度变化率 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]