改进SURF在多模MRI乳腺配准算法中的研究  

Research on Improved SURF Breast Registration Algorithm in Multi-Mode MRI

作　　者：李积英[1] 杨永红 温强 王燕[1] 杨宜林 Li Jiying;Yang Yonghong;Wen Qiang;Wang Yan;Yang Yilin(School of Electronics and Information Engineering,Lanzhou Jiaotong University,Lanzhou,Gansu 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《激光与光电子学进展》2020年第12期154-162,共9页Laser & Optoelectronics Progress

摘　　要：加速稳健特征算法在多模核磁共振成像(MRI)肿瘤图像配准中,存在特征点偏少且配准精度低等问题.使用Harris角点检测法对参考图像、浮动图像的特征点进行提取和检测,接着使用圆形64维向量法生成特征描述符并进行欧氏距离匹配,来增强特征点的提取.通过设置配准图像初始化参数,确保粒子在最优值附近搜索,利用互信息作为粒子群优化算法的测度函数,增强目标函数全局最优解,通过引入平均最值,防止算法陷入早熟现象.仿真结果表明,与现有的算法相比,所提优化算法可以使多模MRI图像特征点增多且精度更高.For the problem of a few feature points and low registration accuracy in multi-mode magnetic resonance imaging(MRI)tumor image registration by speeded up robust features algorithm.Harris detection method is used to extract and detect the feature points of reference image and floating image,and then circular 64-dimensional vector method is used to generate feature descriptors and carry out Euclidean distance matching to enhance the extraction of feature points.The initial parameters of registration image are set to ensure that particles search near the optimal value.The mutual information is used as the measure function of particle swarm optimization to enhance the global optimal solution of the objective function.The average maximum value is introduced to prevent this algorithm from falling into premature phenomenon.Simulation results show that compared with the existing algorithms,the proposed optimization algorithm can increase the number of multi-mode MRI image feature points with higher accuracy.

关 键 词：图像处理 多模核磁共振成像配准 互信息 加速稳健特征算法 HARRIS角点检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]