快速Arclet变换及其在工业CT图像圆测量中的应用

李宗剑; 邹晓兵

doi:10.3788/OPE.20152308.2400

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快速Arclet变换及其在工业CT图像圆测量中的应用

信息科学 | 更新时间：2020-08-12

- 快速Arclet变换及其在工业CT图像圆测量中的应用
- Fast Arclet transform and its application in circle measurement of industrial CT images
- 光学精密工程 2015年23卷第8期页码：2400-2406
- 作者机构：
  
  重庆第二师范学院数学与信息工程系重庆,400067
- 作者简介：
  
  [ "李宗剑(1981-),男,重庆人,博士,副教授,2004年、2007年、2010年于重庆大学分别获得理学学士、理学硕士、工学博士学位,主要从事数字图像处理、模式识别的研究。E-mail:joyli11@sohu.com" ]
  [ "邹晓兵(1974-),男,四川大竹人,博士,副教授,1997年于重庆师范大学获得理学学士学位,2007年、2010年于重庆大学分别获得理学硕士、工学博士学位,主要从事CT扫描方式、成像算法的研究。E-mail:xiaobingzou@163.com" ]
- 基金信息：
  
  重庆市教委科学技术研究项目(No.KJ131505,No.KJ121507)();重庆市基础与前沿研究计划资助项目(No.cstc2014jcyjA40040)()
- DOI：10.3788/OPE.20152308.2400
  中图分类号： TP391;TG115.281
- 收稿日期：2015-05-07，
  
  修回日期：2015-06-09，
  
  纸质出版日期：2015-08-25
- 稿件说明：
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李宗剑, 邹晓兵,. 快速Arclet变换及其在工业CT图像圆测量中的应用[J]. 光学精密工程, 2015,23(8): 2400-2406

LI Zong-jian, ZOU Xiao-bing,. Fast Arclet transform and its application in circle measurement of industrial CT images[J]. Editorial Office of Optics and Precision Engineering, 2015,23(8): 2400-2406
李宗剑, 邹晓兵,. 快速Arclet变换及其在工业CT图像圆测量中的应用[J]. 光学精密工程, 2015,23(8): 2400-2406 DOI： 10.3788/OPE.20152308.2400.

LI Zong-jian, ZOU Xiao-bing,. Fast Arclet transform and its application in circle measurement of industrial CT images[J]. Editorial Office of Optics and Precision Engineering, 2015,23(8): 2400-2406 DOI： 10.3788/OPE.20152308.2400.

摘要

为了准确描述工业CT图像中的圆形结构

提出一种基于多尺度几何分析工具Arclet的圆测量算法。首先

通过分析单尺度Arclet基函数间的空间关系

设计了单尺度快速Arclet数字变换算法;基于该Arclet数字变换

提取出单尺度上的候选圆特征。然后

考虑Arclet相邻尺度基函数间的空间关系并结合多尺度四叉树结构

按照从根到叶的方向

依次对相邻尺度间的候选圆特征进行取舍。最后

融合各尺度剩余的候选圆特征

得到提取结果

并根据提取结果计算出半径等圆参数。基于实际工业CT图像进行了圆测量试验

结果表明

最大半径绝对误差的绝对值< 0.1 mm

最大半径相对误差的绝对值< 0.5%;即使对原工业CT图像引入不同强度的高斯噪声

测量结果依然满足要求。本文提出的工业CT图像圆测量算法对噪声干扰具有很好的抑制能力

能满足准确描述工业CT图像中圆形结构的要求。

Abstract

To accurately describe the circular structures of industrial CT images

a measurement algorithm based on multi-scale geometric analysis tool

named Arclet

was presented. Firstly

a fast mono-scale Arclet numeric transform algorithm was designed through analysis of the spatial relationship between mono-scale Arclet basis functions. Then

the candidate circular features at mono-scale were extracted based on the mono-scale numeric transform transform. By considering the spatial relationship between Arclet basis functions at adjacent scales and the multi-scale tree structure

the candidate circular features between adjacent scales were chosen from a root to leafs. Finally

the surplus circular feature of each scale was fused to obtain the extracted results

and to calculate the related parameters such as radius based on the extracted results. The circle measurement for an actual industrial CT image was performed and the measurement results show that the absolute value of maximum radius absolute error is less than 0.1 mm and that of maximum radius relative error is less than 5%. Even if adding the Gauss noise of different intensities into the original industrial CT image

the measurement results are still can meet the requirements. The measurement algorithm based on fast Arclet transform has better ability to inhibit the noise interference and meet the requirements of industrial CT images for circular structure description.

关键词

Keywords

references

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