期刊文献+

基于多尺度形态学梯度的医学图像边缘检测 被引量:8

Medical image edge detection based on multi-scale morphological gradient
下载PDF
导出
摘要 边缘检测是数字图像处理的一个重要内容,经典的边缘检测算子算法主要采用Prewitt算子、LOG算子、Canny算子等在空域中进行。数学形态学利用结构元素去探测图像,在讨论形态腐蚀和形态膨胀的基础上,提出了一种基于多尺度形态学梯度的医学图像边缘检测算法。单尺度形态学基元随着尺度的增大形成新的更大尺寸的结构元素,从而检测不同的边缘信息,最终重建较理想的图像边缘。仿真结果表明,该算法在含噪图像中能得到较为理想的图像边缘信息,其抗噪声性能明显优于经典的算子检测算法,检测精度较经典的单一梯度算子检测方法亦有一定的改善。 Edge detection is one of the important roles in the digital image processing, the Prewitt operator, LOG operator, Canny operator are adopted in the classical edge detection operator arithmetic in space domain. Mathematic morphology is utilized in digital image processing, among which the basic method is detecting image based on structural elements. A kind of image edge detection arithmetic is addressed based on multi-scale morphological gradient which is set on the morphological erosion and morphological dilation. Different kind of shape structural element can detect edge information which will rebuild ideal image edge. The simulation results show that the arithmetic can get ideal image edge information in noise image, among which the performance of anti-noise is obviously better than the classical operator detection arithmetic, and this kind of arithmetic is improved which is better than classical single operator arithmetic in the aspect of detection precision as well.
出处 《计算机工程与设计》 CSCD 北大核心 2008年第4期888-890,893,共4页 Computer Engineering and Design
基金 湖北省教育厅自然科研基金项目(D200513001)
关键词 数学形态学 图像处理 多尺度梯度 边缘检测 含噪图像 mathematical morphology image processing multi-scale morphological gradient edge detection noise image
  • 相关文献

参考文献8

二级参考文献30

共引文献192

同被引文献42

  • 1方磊.基于数学形态学的多尺度熵权边缘检测方法[J].计算机时代,2009(1):45-46. 被引量:2
  • 2亓磊,吴晓娟,张元元,巴本冬.一种基于亚像素边缘特征的医学图像融合方法[J].电子技术应用,2007,33(7):61-63. 被引量:2
  • 3章毓晋.图像工程(下册)--图像理解与计算机视觉.北京:清华大学出版社,1999.
  • 4章毓晋.图像工程(上册)-图像分析和处理[M].北京:清华大学出版社,1999.181-192.
  • 5ROTHER C, KOLMOGOROV V, BLAKE A. GrabCut: interactive foreground extraction using iterated graph cuts [ J]. ACM Transac- tions on Graphics, 2004, 23(3): 309-314.
  • 6CHEN D, CHEN B, MAMIC G, et al. Improved GrabCut segmentation via GMM optimization [ C] // Proceedings of the 2008 International Con- ference on Digital Image Computing: Techniques and Applications. Washington, DC: IEEE Computer Society, 2008:39-45.
  • 7HANS D, TAO W B, WANG D S, et al. Image segmentation based on GrabCut framework integrating multiscale nonlinear struc- ture tensor [ J]. IEEE Transactions on Image Processing, 2009, 18 (10) : 2289 -2302.
  • 8王钧铭,高立鑫,赵力.基于分水岭预分割的Grabcut算法[J].声学技术,2008,27(4):179-182.
  • 9VINCENT L, SOILLE P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations [ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6) : 583 - 598.
  • 10She Fenghua, Chen Ronghua, Gao Weimin. Improved 3D thinning algorithms for skeleton extraction [C]. llth Conference on Digital Image Computing: Techniques and Applications, Melbourne, AUSTRALIA, 2009.

引证文献8

二级引证文献58

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部