计算机应用 ›› 2011, Vol. 31 ›› Issue (04): 1027-1029.DOI: 10.3724/SP.J.1087.2011.01027

• 图形图像技术 • 上一篇    下一篇

基于分数阶微分增强的肺CT图像血管分割

赖均1,2,解梅1   

  1. 1. 电子科技大学 电子工程学院,成都 610054
    2. 重庆邮电大学 计算机科学与技术学院,重庆 400065
  • 收稿日期:2010-10-11 修回日期:2010-11-15 发布日期:2011-04-08 出版日期:2011-04-01
  • 通讯作者: 赖均
  • 作者简介:赖均(1970-),男,四川宜宾人,讲师,博士研究生,主要研究方向:图像处理、模式识别、软件工程;
    解梅(1956-),女,北京人,教授,博士,主要研究方向:图像处理、模式识别、小波理论。
  • 基金资助:
    重庆邮电大学自然科学基金资助项目(A2009-58)

Vascular segmentation for lung CT images based on fractional differential enhancement

Jun LAI1,2,Mei XIE2   

  1. 1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    2. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 610054, China
  • Received:2010-10-11 Revised:2010-11-15 Online:2011-04-08 Published:2011-04-01
  • Contact: Jun LAI

摘要: 为了提高对肺CT图像中血管自动分割的准确性,提出基于分数阶微分增强的局部子区域分割方法。通过对肺CT图像的增强、分割方法和分数阶微分对图像细微细节的增强能力的比较和研究后, 该方法先采用构建的分数阶微分算子对肺CT图像加以增强后, 再用两个控制指标获取的局部区域最优阈值来分割肺血管。实验结果表明, 它可以有效地提取肺血管网络并且能够分割得到更为丰富的血管细节; 对比传统方法的肺血管分割结果,它能更准确地分割出肺CT图像中的血管。

关键词: 分数阶微分, 增强, 分割, 最优阈值, 血管

Abstract: In order to improve the accuracy of automatic vascular segmentation in CT lung images, after researching image enhancement, segmentation method and the enhancing ability of the fractional differential operator toward image details, a local sub-regional segmentation method based on fractional differential enhancing was proposed. This method enhanced the lung CT images with fractional differential operator firstly, and then the optimal threshold acquired under two control indexes was used for vascular segmentation in the local region. The experimental results show that the proposed method can effectively extract the vascular network in which the blood vessels have more detailed information. And compared to traditional pulmonary vascular segmentation method, it has more accurate segmentation ability of the pulmonary vascular in CT images.

Key words: fractional differential, enhancement, segmentation, optimal threshold, vascular

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