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Image registration by maximization of mutual information based on edge width matching using particle swarm optimization

机译:通过基于粒子群优化的边缘宽度匹配实现互信息最大化的图像配准

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摘要

Mutual information (MI) based image registration has been found to be quite effective in many medical image applications. However, standard MI hampers the convergence of registration transformation parameters since it contains local maxima. In this paper, a novel registration method is proposed. At first, MI based on edge width matching is computed to avoid great change of joint probability distribution and get less local maxima. Particle swarm optimization (PSO), which combines local search methods with global ones balancing exploration and exploitation, is done to search the optimal registration parameter. PSO has less computational complexity as its complex behavior follows only a few simple rules. It could avoid local maxima and reach global optimal results. This method is applicable to a variety of multimodal images, and suitable to different interpolation methods. Theoretical analysis and experiments show that this method is effective and accurate to register multimodal medical images.
机译:已经发现基于互信息(MI)的图像配准在许多医学图像应用中非常有效。但是,由于标准MI包含局部最大值,因此会妨碍注册转换参数的收敛。本文提出了一种新颖的注册方法。首先,计算基于边缘宽度匹配的MI,以避免联合概率分布发生较大变化并获得较小的局部最大值。完成了将局部搜索方法与全局探索与开发相结合的粒子群优化算法(PSO),以搜索最佳配准参数。由于PSO的复杂行为仅遵循一些简单规则,因此其计算复杂度较低。它可以避免局部最大值并达到全局最佳结果。该方法适用于各种多峰图像,并且适用于不同的插值方法。理论分析和实验表明,该方法对多模态医学图像的配准是有效和准确的。

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