| 基于海明窗滤波及粒子群优化搜索的医学图像配准 |
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| 引用本文： | 裴继红,田剑豪,杨烜. 基于海明窗滤波及粒子群优化搜索的医学图像配准[J]. 生物医学工程学杂志, 2007, 24(2): 262-267 |
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| 作者姓名： | 裴继红 田剑豪 杨烜 |
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| 作者单位： | 深圳大学,信息工程学院,深圳,518060 |
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| 摘 要： | 针对互信息在多模态医学图像配准中的局部极值问题,利用海明窗(Hamming窗)进行滤波预处理,并采用粒子群优化(Particle swarm optimization,PSO)方法搜索配准参数。结果表明,图像经过Hamming窗低通滤波后,局部极值大大减少,有利于利用互信息进行图像的配准。另外,PSO优化算法在大多数情况下都可以收敛到全局最优解。我们提出的方法可有效克服互信息的局部极值问题,并有效提高配准精度。
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| 关 键 词： | 图像配准 互信息 滤波 粒子群优化 |
| 修稿时间： | 2004-09-012005-03-31 |
Medical Image Registration Based on Hamming Window Filtering And Particle Swarm Optimizaton |
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| Pei Jihong,Tian Jianhao,Yang Xuan. Medical Image Registration Based on Hamming Window Filtering And Particle Swarm Optimizaton[J]. Journal of biomedical engineering, 2007, 24(2): 262-267 |
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| Authors: | Pei Jihong Tian Jianhao Yang Xuan |
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| Affiliation: | College of Information Engineering, Shenzhen University, Shenzhen 518060,China |
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| Abstract: | Local maxima in multimodality image registration based on mutual information is discussed in this paper. Particle swarm optimization (PSO) and filter preprocessing based on hamming window is used to search the registration parameters. Simulations have been done to illustrate that after low-pass filter preprocessing local maxima is eliminated to a great extent. In most case the global maxima can be found by PSO. Simulations illustrate the efficiency and accuracy of this method in registration strategy. |
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| Keywords: | Image registration Mutual information Filtering Particle swarm optimization (PSO) |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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