Multi-modality image registration using gradient vector flow intensity

Authors:
Yujun Guo;Chi-Hsiang Lo;Cheng-Chang Lu
Affiliations:
Department of Computer Science, Kent State University, Kent, OH;Department of Electronic Engineering, National Ilan University, Ilan, Taiwan;Department of Computer Science, Kent State University, Kent, OH
Venue:
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Year:
2006

Citing 5
Cited 0

Affine Registration with Feature Space Mutual Information

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Distance-Intensity for image registration

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Multi-modal image registration by quantitative-qualitative measure of mutual information (Q-MI)

CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing
Likelihood maximization approach to image registration

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Similarity measure plays a critical role in image registration. Mutual information (MI) has been proved to be a promising measure used widely in multi-modality image registration. However, applying mutual information to original intensities only takes statistical information into consideration, while spatial information is not even considered. In this paper, a novel approach is proposed to incorporate spatial information into MI through gradient vector flow (GVF). Mutual information now is calculated from the GVF-intensity (GVFI) map of the original images instead of their intensity values. Multi-modality brain image registration was performed to test the accuracy and robustness of the proposed method. Experimental results showed that the success rate of our method is higher than that of traditional MI-based registration.