摘要
现有的医学图像配准算法一般都存在需要人工介入、配准时间过长等问题。为了寻找快速、精确、鲁棒性强的自动配准算法,在采用主轴矩方法进行脑部MR(核磁共振)图像的初始配准的基础上,提出局部搜索算法对图像求得更精确的配准。实验表明,该方法的配准精度和现有的Powell算法都可以达到亚像素级,但局部搜索方法和Powell算法相比较,平均配准时间大大缩短;即便和采用了主轴矩粗配准的Powell算法相比较,配准效率也提高了一倍左右。主轴矩粗配准算法提高了配准效率,局部搜索算法则保证了配准的精度。
The existing algorithms for medical image registration always require human intervention and too much time. In order to develop a fast, accurate and anti - noise automated image registration algorithm, the Cross - Weighted Moments algorithm is applied to initialize the registration of the brain MR images for enhancing the efficiency and a local search algorithm is proposed to achieve more accurate registration. The experiments show that the local search algorithm and the Powell algorithm both can reach the sub - pixel level of accuracy but the local search algorithm can shorten much time. Compared with the Powell algorithm which is combined with the Cross - Weighted Moments initialization, the runing time is nearly half shortened.
出处
《计算机仿真》
CSCD
2007年第4期61-63,99,共4页
Computer Simulation
关键词
图像配准
互信息
主轴矩
局部搜索
Image registration
Mutual information
Cross - weighted moments
Local search
