摘要
SIFT算法是提取特征点的常用算法之一,具有良好的健壮性,在医学图像处理领域中广泛应用。然而传统的SIFT算法采用固定的降噪阈值会影响特征点选取的合理性,因此提出了一种动态优化算法(PM-SIFT算法),即采用PSO算法与互信息结合的方式优化传统SIFT算法,并从特征点有效性与计算时效性两方面对2种算法的特征点提取效果进行定量分析的结果表明,与传统的SIFT算法相比,PM-SIFT算法不但可以动态地选取合适的降噪阈值,而且还能提高图像配准的效果。
Scale invariant feature transform( SIFT) algorithm,one of the commonly used algorithms for extraction of feature points,is of good robustness and widely applied in medical image processing. However,the invariant denoising threshold of traditional SIFT algorithm will affect the rationality to select its feature points,a dynamic optimization algorithm,PM-SIFT algorithm which optimizes the traditional SIFT algorithm by combining PSO algorithm with mutual information,was thus proposed in this paper. The feature pints extracted using the two algorithms were compared in terms of the availability of their feature points and the effectiveness of their computation. Quantitative analysis showed that the PM-SIFT algorithm can not only dynamically select appropriate medical image denoising threshold but also improve the effectiveness of medical image match as compared with the traditional SIFT algorithm.
作者
赵悟
段永璇
肖宪翠
张睿
席敏
ZHAO Wu;DUAN Yong-xuan;XIAO Xian-cui;ZHANG Rui;XI Min(Shandong Medical and Health Information Institute,Shandong No.1 Medical University or Shandong Academy of Medical Sciences,Jinan 250062,Shandong Province,China;Shandong Normal University Information Science and Engineering School,Jinan 250014,Shandong Province,China)
出处
《中华医学图书情报杂志》
CAS
2020年第4期72-76,共5页
Chinese Journal of Medical Library and Information Science
基金
国家自然基金项目“网络资源协同管理与多目标虚拟网络映射问题研究”(61373149)
山东省医学科学院面上项目“医学信息及数据服务云平台建设研究”(2016-01)
山东省医药卫生科技发展计划项目“基于统计学习算法的精准疾病知识库构建研究”(2018WSA18030)。
关键词
SIFT算法
PSO算法
互信息
降噪阈值
特征点有效性
计算时效性
SIFT algorithm
PSO algorithm
Mutual information
Denoising threshold
Availability of feature points
effectiveness of computation
