Medical image registration is important in many medical applications. Registration method based on maximization of mutual information of voxel intensities is one of the most popular methods for 3-D multi-modality medi...Medical image registration is important in many medical applications. Registration method based on maximization of mutual information of voxel intensities is one of the most popular methods for 3-D multi-modality medical image registration. Generally, the optimization process is easily trapped in local maximum, resulting in wrong registration results. In order to find the correct optimum, a new multi-resolution approach for brain image registration based on normalized mutual information is proposed. In this method, to eliminate the effect of local optima, multi-scale wavelet transformation is adopted to extract the image edge features. Then the feature images are registered, and the result at this level is taken as the initial estimate for the registration of the original images. Three-dimensional volumes are used to test the algorithm. Experimental results show that the registration strategy proposed is a robust and efficient method which can reach sub-voxel accuracy and improve the optimization speed.展开更多
基金Supported by National Natural Science Foundation of China (No.60373061)Natural Science Foundation of Tianjin (No.04310491R)+1 种基金National Natural Science Foundation of ChinaGeneral Administration of Civil Aviation of China (No.60372048) .
文摘Medical image registration is important in many medical applications. Registration method based on maximization of mutual information of voxel intensities is one of the most popular methods for 3-D multi-modality medical image registration. Generally, the optimization process is easily trapped in local maximum, resulting in wrong registration results. In order to find the correct optimum, a new multi-resolution approach for brain image registration based on normalized mutual information is proposed. In this method, to eliminate the effect of local optima, multi-scale wavelet transformation is adopted to extract the image edge features. Then the feature images are registered, and the result at this level is taken as the initial estimate for the registration of the original images. Three-dimensional volumes are used to test the algorithm. Experimental results show that the registration strategy proposed is a robust and efficient method which can reach sub-voxel accuracy and improve the optimization speed.
