摘要
将基于动态融合的蚁群遗传算法作为一种新的图像配准优化算法应用在多模医学图像配准中。该算法以互信息作为相似性测度,生成初始信息素分布,采用蚁群算法搜索最优变换参数,其中动态融合策略提高了混合算法的搜索效率。仿真实验结果表明,该算法有效地避免信息函数的局部极值,减少大量重复运算,提高了配准的效率,配准结果具有良好的稳定性。
This paper advances a new optimization algorithm of image registration based on the dynamic combination of genetic algorithm and ant colony algorithm, then it is applied to multimodality medical image registration. This algorithm applied mutual information as the similarity measurement, firstly generates initialization pheromone distribute, then searches the best parameters using genetic algorithm, in the algorithm the strategy of dynamic fusion improves efficiency of searching. Experimental results show that the new registration method can efficiently restrain the local maxima of mutual information function, avoid vast repeated calculations, improve the efficiency of image registration. Furthermore it is excellent in robustness.
出处
《计算机工程》
CAS
CSCD
北大核心
2008年第1期227-229,235,共4页
Computer Engineering
基金
“985工程”信息化基础结构关键技术科技创新平台基金资助重点项目
关键词
蚁群算法
遗传算法
动态融合
互信息
图像配准
ant colony algorithm
genetic algorithm
dynamic combination
mutual information
image registration
