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中文摘要：
医药图象登记在许多医药应用程序是重要的。登记方法基于 voxel 紧张的相互的信息的最大化是为 3-D 多形式的最流行的方法之一医药图象登记。通常，优化过程容易在本地最大值被套住，导致错误的登记结果。以便发现正确最佳，为大脑图象登记的一条新多决定途径基于规范的相互的信息被建议。在这个方法，消除本地 optima 的效果，多尺度的小浪转变被采用提取图象边特征。然后，特征图象被登记，并且在这水平的结果为原来的图象的登记作为起始的估计被拿。三维的体积被用来测试算法。登记策略建议了的试验性的结果表演是能到达 sub-voxel 精确性并且改进优化速度的一个柔韧、有效的方法。
英文摘要：
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.
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