摘要
目的研究用梯度矢量流与粒子群优化算法实现多模态医学图像配准,提高配准的精度。方法算法对图像配准的特征空间、相似性测度、搜索策略3个方面进行改进:先由原始图像产生梯度矢量流场,作为配准的特征空间;然后提出并计算3种基于梯度矢量流场的相似性测度;最后使用结合了遗传算法交叉机制的粒子群优化算法找到两幅图像的最优变换。结果对仿真及实际医学图像的54次配准实验,表明该方法配准精度优于基于像素的粒子群优化方法和Walsh变换法。结论基于梯度矢量流与粒子群优化算法的图像配准方法能有效地实现多模态医学图像的配准。
Objective To study the method based on gradient vector flow （GVF） and particle swarm optimization （PSO） for realizing multimodal medical image registration and improving its accuracy. Methods In view of three major components of image registration, i.e. the feature space, the similarity metric and the search strategy, a novel method was proposed with three improvements. Firstly, the GVF field was employed as the feature space. Then three similarity metrics were proposed based on GVF field. Finally, an improved PSO combined with crossover mechanism of genetic algorithm was utilized to search for the optimal transformation of two images. Results With 54 times of experiments on both simulated and real medical images, it was demonstrated that this method accurately registered the multimodal medical images to be superior to the method based on PSO of pixels, and the Walsh transform method. Conclusion The method based on GVF and PSO is effective for multimodal medical image registration.
出处
《航天医学与医学工程》
CAS
CSCD
北大核心
2008年第6期505-512,共8页
Space Medicine & Medical Engineering
基金
国家重点基础研究规划基金项目(2006CB705707)
国家自然科学基金项目(30570488)
上海市重点学科建设项目(B112)
关键词
图像配准
多模态医学图像
梯度矢量流
粒子群优化
image registration
multimodal medical images
gradient vector flow
particle swarm optimization
