摘要
为了有效地融合解剖和功能医学图像中的信息,提出一种基于窗口标准差的小波系数自适应加权平均融合算法.它的优点在于能动态地在系数之间分配权重,从而达到提高融合效果的目的.实验结果表明该方法优于传统的加权平均系数组合方法.
In order to fuse the information from anatomical and functional medical images effectively,it is proposed a fusion algorithm of wavelet coefficients adaptive weighted averaging based on window-based standard deviation .Its advantage consists in being able to distribute the weights between coefficients dynamically and achieve good fusion result.Experimental results show that the proposed approach outperforms the conventional coefficient combining method of weighted averaging.
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2005年第3期200-205,共6页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金资助项目(60162001).
关键词
小波变换
医学图像融合
基于窗口的标准差
自适应加权平均
wavelet transform
medical image fusion
window-based standard deviation
adaptive weighted averaging
