结合遗传思想蝙蝠算法甲状腺SPECT-B超图像配准
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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.
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.
引文
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[3]Knops Z F,Maintz J B A,Viergever M A,et al.Normalized mutual information based registration using K-means clustering and shading correction[J].Medical Image Analysis(S1361-8415),2006,10(3):432-439.
[4]Pluim J P,Maintz J B,Viergever M A.Image registration by maximization of combined mutual information and gradient information[J].Transactions on Medical Imaging(S0278-0062),2000,19(8):809-814.
[5]YANG Xinshe.Bat Algorithm:Literature Review and Applications[J].Bio-Inspired Computation(S1758-0366),2013,5(3):141-149.
[6]肖辉辉,段艳明.改进的蝙蝠算法在数值积分中的应用研究[J].智能系统学报,2014,9(3):364-371.XIAO Huihui,DUAN Yanming.Application of the improved bat algorithm in numerical integration[J].CAAI Transactions on Intelligent Systems,2014,9(3):364-371.
[7]谢建,周永权,陈欢.一种基于Lévy飞行轨迹的蝙蝠算法[J].模式识别与人工智能,2013,26(9):829-837.XIE Jian,ZHOU Yongquan,CHEN Huan.A bat algorithm based on Lévy flights trajectory[J].PR&AI,2013,26(9):829-837.
[8]Easley G,Labate D,Lim W Q.Sparse directional image representations using the discrete shearlet transform[J].Applied and Computational Harmonic Analysis(S1063-5203),2008,25(1):25-46.
[9]XU Ning,Ahujab N,Bansalc R.Object segmentation using graph cuts based active contours[J].Computer Vision and Image Understanding(S1077-3142),2007,107(3):210-224.
[10]YANG Xinshe.A New Metaheuristic Bat-Inspired Algorithm[C]//Inspired Cooperative Strategies for Optimization(NICSO 2008),Berlin,Germany,Apr 23-16,2010,284:65-74.
[11]潘勇,郭晓东.一种基于遗传算法改进的粒子群优化算法[J].计算机应用与软件,2011,28(9):222-224.PAN Yong,GUO Xiaodong.An improved particle swarm optimization algorithm based on genetic algorithm[J].Computer Applications and Software,2011,28(9):222-224.
[12]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on Systems,Man and Cybernetics.Part B:Cybernetics(S1083-4419),1996,26(1):29-41.
[13]唐红梅,宋培姣,王霞,等.基于改进粒子群优化算法的互信息图像配准[J].电视技术,2011,35(23):8-11.TANG Hongmei,SONG Peijiao,WANG Xia,et al.Mutual information image registration based on improved particle swarm optimization algorithm[J].Ideo Engineering,2011,35(23):8-11.
[14]郑伟,孟繁婧,田华,等.基于混合蛙跳算法的SPECT-B超甲状腺图像配准[J].河北大学学报:自然科学版,2013,33(3):305-311.ZHENG Wei,MENG Fanjing,TIAN Hua,et al.Registration of SPECT image and B-type ultrasound image based on shuffled frog leaping algorithm[J].Journal of Hebei University:Natural Science Edition,2013,33(3):305-311.
[15]王东峰,邹谋炎.基于最大对齐度的多模态图像自动配准[J].电子与信息学报,2003,25(6):755-762.WANG Dongfeng,ZOU Mouyan.Automatic registration of multi-modal images based on the maximization of the alignment metric[J].Journal of Electronics&Information Technology,2003,25(6):755-762.
[16]Rueckert D,Sonoda L,Hayes C,et al.Nonrigid registration using free-form deformations:application to breast MR images[J].IEEE Transactions on Medical Imaging(S0278-0062),1999,18(8):712-721.