东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (8): 1107-1110.DOI: -

• 论著 • 上一篇    下一篇

双向梯度归一化互信息医学图像配准方法

潘晓光;李宏;康雁;刘积仁;   

  1. 东北大学信息科学与工程学院;东北大学中荷生物医学与信息工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(61071213,51009016)

Bilateral medical image registration based on normalized mutual information combined with gradient information

Pan, Xiao-Guang (1); Li, Hong (2); Kang, Yan (2); Liu, Ji-Ren (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Kang, Y.
  • About author:-
  • Supported by:
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摘要： 传统互信息配准方法未利用图像的空间信息,为此,提出一种将互信息与梯度相似性相结合的双向医学图像配准方法.首先以图像A为参考,求图像A和待配准图像B的每组对应点的梯度相似性,并在计算相似性之前引入高斯算子以降低噪声影响,将梯度相似性因子与归一化互信息的乘积作为图像配准的正向测度;反过来,再以图像B为参考计算逆向的梯度归一化互信息.由此得到双向梯度归一化互信息.实验结果表明,该方法比传统归一化互信息和梯度归一化互信息方法有更高的鲁棒性和精度.

关键词: 双向图像配准, 梯度相似性, 互信息, 高斯算子, 多模图像

Abstract: Combining mutual information of an image with its gradient information, a new bilateral image registration method is developed to use the image's spatial information conventional registration methods neglect. Taking an image A as a reference, the gradient similarities are calculated between the corresponding points of image A and B to be registered after a Gaussian operator is introduced to reduce the effect of noises, and the normalized mutual information multiplied by the gradient similarity is named "forward measure". Conversely, using the image B as a reference, the gradient similarity is thus calculated, thereby "backward measure" being determined. Then, the normalized mutual information combined with bilateral gradient similarity is obtained. Experimental results show that the new method is more robust and accurate than the conventional ones.

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