摘要
为了提高甲状腺肿瘤检出的准确率,将遗传思想中的选择、交叉和变异操作引入蝙蝠算法,提出基于遗传思想蝙蝠算法的甲状腺SPECT-B超图像配准。首先,利用甲状腺SPECT图像与B超图像共有的甲状腺及肿瘤轮廓特征,以改进的梯度归一化互信息为相似性测度,以遗传思想蝙蝠算法为优化策略优化配准所需的空间变换参数。实验结果表明,改进的梯度归一化互信息具有高定位精度和较少的局部极值;遗传思想蝙蝠算法提高收敛速度与收敛精度,改进的梯度归一化互信息与遗传思想蝙蝠算法相结合使甲状腺SPECT-B超图像较好地配准。
In order to improve the accuracy in thyroid tumor detection, the selection, crossover and mutation operation of genetic idea was introduced into the bat algorithm and a novel registration method of SPECT image and B-type ultrasound image was proposed based on genetic idea bat algorithm. First, according to the common contour characteristic of thyroid and tumor, improved gradient normalized mutual information was used for similarity measurement. Second, the genetic idea bat algorithm was used to optimize the space registration transform parameters. The experimental results show that improved gradient normalized mutual information has high locating accuracy of registration and less local extremum, genetic idea bat algorithm improves the rate and accuracy of convergence of registration. The method achieves desired effect in registration of SPECT image and B-type ultrasound image.
出处
《光电工程》
CAS
CSCD
北大核心
2015年第12期67-73,81,共8页
Opto-Electronic Engineering
基金
河北大学医工交叉研究中心开放基金项目(BM201103)
关键词
图像配准
甲状腺肿瘤
遗传思想
蝙蝠算法
梯度归一化互信息
image registration
thyroid tumor
genetic idea
bat algorithm
gradient normalized mutual information
