中国组织工程研究

中国组织工程研究 ›› 2012, Vol. 16 ›› Issue (22): 4109-4111.doi: 10.3969/j.issn.1673-8225.2012.22.028

• 数字化骨科 digital orthopedics • 上一篇    下一篇

基于改进SUSAN算法的医学图像边缘检测★

王  敏1，龚晓峰1，曾  军2   

  1. 1四川大学电气信息学院自动化系，四川省成都市 610065；2中国兵器工业集团第209研究所，四川省成都市610041
  • 收稿日期:2011-10-05 修回日期:2011-11-17 出版日期:2012-05-27 发布日期:2012-05-27
  • 通讯作者: 龚晓峰，硕士生导师，教授，四川大学自动化系，四川省成都市 610065 gongxiaofeng@cdhuari.com
  • 作者简介:王敏★，女，1987年出生，四川省泸州市人，汉族， 四川大学在读硕士，主要从事控制工程和信号、图像处理方面的研究。 wangmin6727@126.com

Medical image edge detection based on the improved SUSAN algorithm

Wang Min1, Gong Xiao-feng1, Zeng Jun2   

  1. 1Department of Automation, Electrical and Information Engineering of Sichuan University, Chengdu  610065, Sichuan Province, China; 2China North Industries Group 209 Research Institute, Chengdu  610041, Sichuan Province, China
  • Received:2011-10-05 Revised:2011-11-17 Online:2012-05-27 Published:2012-05-27
  • Contact: Gong Xiao-feng, Master’s supervisor, Professor, Department of Automation, Electrical and Information Engineering of Sichuan University, Chengdu 610065, Sichuan Province, China gongxiaofeng@cdhuari.com
  • About author:Wang Min, Studying for master’s degree, Department of Automation, Electrical and Information Engineering of Sichuan University, Chengdu 610065, Sichuan Province, China wangmin6727@126.com

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摘要：

背景：医学图像的边缘检测是医学图像处理中的一项重要的技术，也是医学图像进一步处理的基础。
目的：运用改进的SUSAN算法对医学图像进行边缘检测，取得更丰富的医学图像边缘信息，以便于医学图像的进一步处理。
方法：运用Sobel算子对SUSAN算法进行了改进，采用C++语言编程，并在VC++6.0开发平台上实现了改进算法。
结果与结论：实验结果表明，该算法能实现阈值的自适应选取，对医学图像中的低对比度的图像边缘有较好的检测效果。
 

关键词: 医学图像边缘检测, SUSAN算法, Sobel算子, 阈值自适应, 梯度模

Abstract:

BACKGROUND: Medical image edge detection is an important technology of medical image process, which is also the foundation of the deeper medical image process.
OBJECTIVE: To use improved SUSAN algorithm to detect the medical image edge, so that richer medical image edge will be get and which is benefit for the deeper process of medical images.
METHODS: Sobel operator was used to improve the SUSAN algorithm and the C++ programming language was adopted. The improved algorithm was realized based on the VC++6.0 development platform.
RESULTS AND CONCLUSION: Experimental results showed that our algorithm could realize the threshold adaptive selection and had a better detection result in low contrast image edge of medical images.

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