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Research Article | Open Access |

A Registration Method for Multimodal Medical Images Using Contours Mutual Information

Author 1: Ying Qian Author 2: Meng Li Author 3: Qingjie Wei Author 4: Xuemei Ren
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 3, No. 4 · Published 2014 · Cited by 7

DOI: 10.14569/IJARAI.2014.030401

Abstract

In recent years, mutual information has developed as a popular image registration measure especially in multimodality image registration. For different modality medical images, the contour of tissues or organs is similarity. In this paper, an effective new registration method of the multimodal medical images based on the contour mutual information is proposed. Firstly, get the contour through variational level set method. Secondly, within the contour pixels are assigned the same grayscale value, obtain two contour images. Finally, two contour images using mutual information as similarity measure for image registration. The experiment results show that the registration algorithm proposed in this paper can do more effectively and more accurately work than normalized mutual information and gradient mutual information.

Keywords

How to Cite this Article

Qian, Y., Li, M., Wei, Q., & Ren, X. (2014). A Registration Method for Multimodal Medical Images Using Contours Mutual Information. International Journal of Advanced Research in Artificial Intelligence, 3(4). https://doi.org/10.14569/IJARAI.2014.030401

Qian, Ying, et al.. "A Registration Method for Multimodal Medical Images Using Contours Mutual Information." International Journal of Advanced Research in Artificial Intelligence, vol. 3, no. 4, 2014, https://doi.org/10.14569/IJARAI.2014.030401.

@article{Qian2014,
  title     = {A Registration Method for Multimodal Medical Images Using Contours Mutual Information},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {3},
  number    = {4},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Ying Qian and Meng Li and Qingjie Wei and Xuemei Ren},
  doi       = {10.14569/IJARAI.2014.030401},
  url       = {https://doi.org/10.14569/IJARAI.2014.030401}
}

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