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脑磁成像源定位问题的算法研究
其他题名Study of Approaches to MEG Source Localization
罗安
学位类型工学硕士
导师蒋田仔
2002-05-14
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词脑磁成像 偶极子 优化方法 并行计算 概率解 Magnctocncephalography(Meg) Dipolc Optimization Mcthods Parallel Computation Probabilistic Solutions
摘要作者在中科院自动化所攻读硕士期间,主要研究用各种全局优化方法求解脑磁成像 (MEG)的源定位问题的优缺点,并在此基础上,提出了一些解决此问题的新方法。本 文总结了作者的工作。 第一章为绪论部分,介绍了各种脑成像技术的不同功能及其比较,脑磁成像的基本 概念、成像原理和正反向问题的数学模型,并对脑磁成像研究的国际现状进行了综述。 第二章具体介绍了为了解决脑磁成像的源定位问题,我们设计并实现的三种全局算 法——混合遗传算法、模拟退火算法和禁忌搜索算法——的实现细节及其实验结果和分 析。首先,我们把遗传算法和局部搜索策略结合,设计了一种混合遗传算法;其次,禁 忌搜索算法第一次被用于解决这一问题。我们也实现了目前比较常用的模拟退火算法用 于比较算法的性能。实验结果表明混合遗传算法是最有效的方法,禁忌搜索算法也很适 合这一问题。 第三章进一步将混合遗传算法发展到了并行处理器系统实现;对第二章中的模拟退 火算法进行改进以后,也实现了并行化,并进行试验比较了它们的优劣。实验结果证明 并行算法可以成倍提高运算速度,并且并行遗传算法更为优秀。 第四章介绍了MEG源定位问题在模型、其它信息引入和算法方面的最新进展,以及 在以前工作基础上,我们提出了使用并行遗传算法解决贝叶斯框架下求解概率解的方 法。
其他摘要This thesis contributes to the comparison of different global optimization methods to the MEG source localization problem. Moreover, we propose some new approaches to this issue. Our methods perform over the exiting ones. The thesis is organized as follows. Chapter 1 gives the introduction of my work. The functions of different brain image techniques are introduced. Some basic idea of MEG and the formulations of its forward and inverse problem are given. Moreover, the state of the art of the MEG study is reviewed as well. Chapter 2 deals with three typical global optimization methods, i.e., the hybrid genetic algorithm, simulated annealing algorithm and Tabu search algorithm, which are proposed for MEG source localization. The details of our implementation and the experimental results and discussions of them are also given in this chapter. We first introduce a hybrid algorithm by combining genetic and local search strategies. Then, we apply the Tabu search to source localization. Finally, in order to further compare the performance of above algorithms, simulated annealing algorithm, which is popular in this problem, is also applied. The computer simulation results show that the proposed hybrid genetic algorithm is the most effective approach to dipole localization, and the Tabu search algorithm is also a very good strategy for this problem. In chapter 3 the hybrid genetic algorithm is further developed to the parallel computer system. The simulated annealing method described in chapter 2 is improved and parallelized as well. The comparison of parallel genetic algorithm (PGA) and parallel simulated annealing method (PSA) is given. The experimental results show that the parallel computation can improve the computation speed linearly, and the PGA is superior to PSA. Chapter 4 is about the new development in the study of MEG source localization in modeling, the introduction of other information, and the new advancement in algorithms; and on the basis of the previous work, the method proposed by us using PGA to get probabilistic solutions under the Bayesian framework is described and discussed.
馆藏号XWLW633
其他标识符633
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6776
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
罗安. 脑磁成像源定位问题的算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2002.
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