基于GSO算法与互信息的医学图像配准

Medical image registration based on group search optimization algorithm and mutual information

医学图像处理过程通常包括图像预处理、特征提取、图像分类,Harris角点检测算法是常用的特征点提取算法之一。该算法适应多种变换、运算简便,在医学图像处理领域中广泛应用,但在实际应用中发现传统的Harris算法检测到的特征点数量不足且图像配准精度不高。因此提出了一种优化算法(GM-Harris算法),即采用群搜索优化算法(GSO算法)与互信息相结合的方式优化传统Harris算法的过程,并从匹配有效率与算法效率两方面对2种算法的特征点提取效果进行了定量分析。实验结果表明,与传统的Harris算法相比,GM-Harris算法不但可以获得较充足的特征点,而且还能提高图像配准的精度。

Medical image processing usually includes image preprocessing,feature extraction and image classification.Harris angular point detection algorithm,one of the commonly used feature extraction algorithm,is characterized by adaptability to various transformations and simplicity of operation.However,the number of image feature points detected by it is insufficient and the accuracy of its image registration is low.A GM-Harris algorithm which optimizes the process of Harris algorithm by combining the group search optimization(GSO)algorithm with mutual information was thus proposed in order to overcome the shortcomings of traditional Harris algorithm.The feature points detected by the two methods were quantitatively analyzed in aspects of their matching efficiency and algorithm efficiency,which showed that the number of feature points detected by GM-Harris algorithm is greater than that detected by traditional Harris algorithm and the image registration accuracy of GM-Harris algorithm is higher than that of traditional Harris algorithm.