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首页> 外文期刊>Neural Computing & Applications >Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization
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Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization

机译：使用多分辨率方法基于仿射的人脑CT和MR形态图像配准：遗传算法和粒子群优化的比较研究

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摘要

We present a non-linear 2-D/2-D affine registration technique for MR and CT modality images of section of human brain. Automatic registration is achieved by maximization of a similarity metric, which is the correlation function of two images. The proposed method has been implemented by choosing a realistic, practical transformation and optimization techniques. Correlation-based similarity metric should be maximal when two images are perfectly aligned. Since similarity metric is a non-convex function and contains many local optima, choice of search strategy for optimization is important in registration problem. Many optimization schemes are existing, most of which are local and require a starting point. In present study we have implemented genetic algorithm and particle swarm optimization technique to overcome this problem. A comparative study shows the superiority and robustness of swarm methodology over genetic approach.
机译：我们提出了一种非线性的2-D / 2-D仿射配准技术，用于人类大脑部分的MR和CT模态图像。通过最大化相似性度量（两个图像的相关函数）来实现自动配准。通过选择一种现实，实用的转换和优化技术来实现所提出的方法。当两个图像完全对齐时，基于相关的相似性度量应该最大。由于相似性度量是非凸函数，并且包含许多局部最优值，因此选择优化搜索策略对注册问题很重要。现有许多优化方案，其中大多数是局部的，需要一个起点。在目前的研究中，我们已经实现了遗传算法和粒子群优化技术来克服这个问题。一项比较研究表明，群体方法论优于遗传方法。

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