By Topic

A Coarse-to-Refined Approach of Medical Image Registration Based on Combining Mutual Information and Shape Information

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Weiqing Chen ; Minist. of Educ., Dalian Univ. of Technol. ; Zongying Ou ; Weiwei Song

Mutual information (MI) is currently one of the most effective similarity metric in medical image registration. It is an automatic measure, and suitable for multimodal image registration. However it ignores the global spatial information inherent in the images. In addition, the mutual information-based registration is a time-consuming work and can lead to misalignment. A coarse-to-refined medical image registration method is presented in this paper, by combining mutual information with shape information of the images. The gradient images of the registration images generate the principal axes and centroids, and then the two images can be aligned coarsely according to these shape parameters. Mutual information is used to refine the registration. Experiment results demonstrate the presented method reduces the time consumed by 97 percent than the registration using MI alone, which is helpful in clinical applications

Published in:

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on  (Volume:2 )

Date of Conference:

13-15 Oct. 2005