Brain Image Registration Based on Entropy of Mutual Information Matrix

A novel method for high-dimensional mutual information registration is proposed. This method first calculates high-dimensional mutual information matrix, and then calculates the entropy of that matrix. The maximal entropy corresponds to the optimal registration solution. The method was qualitatively and quantitatively evaluated on simulated and real brain images. The obtained results show that the proposed method can improve registration accuracy and decrease registration time.

[1]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[2]  Guy Marchal,et al.  Automated Multimodality Medical Images Registration using Information Theory , 1995 .

[3]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[4]  Max A. Viergever,et al.  f-information measures in medical image registration , 2004, IEEE Transactions on Medical Imaging.

[5]  David J. Hawkes,et al.  Incorporating connected region labelling into automated image registration using mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[6]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[7]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[8]  Charles R. Meyer,et al.  Multi-variate Mutual Information for Registration , 1999, MICCAI.

[9]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[10]  Bo Wang,et al.  A method on calculating high-dimensional mutual information and its application to registration of multiple ultrasound images. , 2006, Ultrasonics.