[1] Chen Xiaoyan, Gu Jia, Li Songyi, Shu Huazhong, et al. A method based on mutual information andgradient information for medical image registration [J]. Journal of Southeast University (English Edition), 2003, 19 (1): 35-39. [doi:10.3969/j.issn.1003-7985.2003.01.009]
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A method based on mutual information andgradient information for medical image registration(
)
一种基于互信息和梯度信息的医学图像配准算法
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 19
- Issue:
- 2003 1
- Page:
- 35-39
- Research Field:
- Biological Science and Medical Engineering
- Publishing date:
- 2003-03-30
Info
- Title:
- A method based on mutual information andgradient information for medical image registration
- 一种基于互信息和梯度信息的医学图像配准算法
- Author(s):
- Chen Xiaoyan; Gu Jia; Li Songyi; Shu Huazhong; Luo Limin
- Department of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
- Keywords:
- medical image registration; gradient information; mutual information; multi-modal images; non-rigid deformation
- PACS:
- R445
- DOI:
- 10.3969/j.issn.1003-7985.2003.01.009
- Abstract:
- Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.
- 由于互信息不需要对图像进行预处理, 因此被广泛地应用于医学图像配准中.但是, 配准过程中的局部极大值难以克服.本文引进了梯度信息, 用于解决局部极值问题.并将这种方法应用到人体的非刚性形变的医学图像配准中.同时, 给出了一些用于改进精度的方法, 如:Powell搜索算法、灰度插值和出界点问题, 提高了匹配精度.采用此方法对脑部和肺部的多模图像进行配准, 实验结果表明该方法对非刚体医学图像的配准有很大的可行性.
References:
[1] Frederik M, Andre C, Dirk V. Multi-modality image registration by maximization of mutual information [J]. IEEE Trans Med Imag, 1997, 16(2): 187-198.
[2] Luo Shuqian, Li Xiang. Multi-modality medical image registration based on maximization of mutual information[J]. Journal of Image and Graphics, 2000, 5(7):551-558.
[3] Chen Yu, Zhuang Tiange. Mutual information based on registration for 3-D non-rigid medical image[J]. Journal of Shanghai Jiaotong University, 1999, 33(9):1125-1127.(in Chinese)
[4] Pluim J P W, Maintz J B A, Viergever M A. Image registration by maximization of combined mutual information and gradient information[J]. IEEE Transactions on Medical Imaging, 2000, 19(8):809-814.
[5] Qin Binjie, Zhuang Tiange. Similarity measures in voxel intensity based 3-D multi-modal medical image registration[J]. Journal of Shanghai Jiaotong University, 2002, 36(7): 942-945.(in Chinese)
[6] Roche A, Pennec X, Malandain G, et al. Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information[J]. IEEE Transactions on Medical Imaging, 2001, 20(10):1038-1049.
Memo
- Memo:
-
Biographies: Chen Xiaoyan(1977—), female, graduate; Shu Huazhong
(corresponding author), male, professor, shu.list@seu.edu.cn.