Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information

Traditional mutual information (MI) function aligns two multimodality images with intensity information, lacking spatial information, so that it usually presents many local maxima that can lead to inaccurate registration. Our paper proposes an algorithm of adaptive combination of intensity and gradient field mutual information (ACMI). Gradient code maps (GCM) are constructed by coding gradient field information of corresponding original images. The gradient field MI, calculated from GCMs, can provide complementary properties to intensity MI. ACMI combines intensity MI and gradient field MI with a nonlinear weight function, which can automatically adjust the proportion between two types MI in combination to improve registration. Experimental results demonstrate that ACMI outperforms the traditional MI and it is much less sensitive to reduced resolution or overlap of images.

[1]  Max A. Viergever,et al.  Comparison of edge-based and ridge-based registration of CT and MR brain images , 1996, Medical Image Anal..

[2]  Irène Buvat,et al.  A Methodology to Validate MRI/SPECT Registration Methods Using Realistic Simulated SPECT Data , 2001, MICCAI.

[3]  Michael Unser,et al.  A pyramid approach to sub-pixel image fusion based on mutual information , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  Paul Suetens,et al.  Medical image registration using mutual information , 2003, Proc. IEEE.

[5]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

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

[7]  Gary E. Christensen,et al.  Consistent landmark and intensity-based image registration , 2002, IEEE Transactions on Medical Imaging.

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

[9]  Hiroyuki Shinohara,et al.  Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images , 2004, Annals of nuclear medicine.

[10]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[11]  Ponnada A Narayana,et al.  Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[12]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[13]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[14]  Max A. Viergever,et al.  Mutual information matching in multiresolution contexts , 2001, Image Vis. Comput..

[15]  Jean-Philippe Thiran,et al.  Affine Registration with Feature Space Mutual Information , 2001, MICCAI.

[16]  Jeffrey Tsao,et al.  Interpolation artifacts in multimodality image registration based on maximization of mutual information , 2003, IEEE Transactions on Medical Imaging.

[17]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[18]  Pierre Hellier,et al.  Coupling dense and landmark-based approaches for nonrigid registration , 2003, IEEE Transactions on Medical Imaging.

[19]  Haiying Liu,et al.  Using Points and Surfaces to Improve Voxel-Based Non-rigid Registration , 2002, MICCAI.

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

[21]  Eldad Haber,et al.  Intensity Gradient Based Registration and Fusion of Multi-modal Images , 2006, MICCAI.

[22]  Max A. Viergever,et al.  Interpolation Artefacts in Mutual Information-Based Image Registration , 2000, Comput. Vis. Image Underst..

[23]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[24]  Paul Suetens,et al.  Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information , 1999, Medical Image Anal..

[25]  Max A. Viergever,et al.  Quantitative evaluation of convolution-based methods for medical image interpolation , 2001, Medical Image Anal..

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