中国医疗设备2016，Vol.31Issue(4)：40-44,5.DOI:10.3969/j.issn.1674-1633.2016.04.008
基于SIFT和SURF的医学图像特征匹配研究
Research on Medical Image Matching Based on SIFT and SURF Features
摘要
Abstract
The paper adopted the matching algorithm based on characteristic points to accomplish image matching in experimental medicine. The two algorithms: scale-invariant feature transform (SIFT) and speeded up robust features (SURF) were compared in aspects of characteristic points, characteristic extraction time and matching veracity. Then the K-nearest neighbor (KNN) algorithm was used to eliminate mismatching points. The paper also carried on statistical analysis of the results of random sample consensus (RANSAC) and least median of squres (LMEDS) as well as the correlation between matching veracity and algorithms. This research established a medical image matching platform based on characteristic points in order to provide a research basis for the further research and improvement of medical image matching.关键词
SIFT/SURF/图像匹配/K最近邻算法Key words
scale-invariant feature transform/speeded up robust features/image matching/K-nearest neighbor algorithm分类
信息技术与安全科学引用本文复制引用
鹿煜炜,胡峻..基于SIFT和SURF的医学图像特征匹配研究[J].中国医疗设备,2016,31(4):40-44,5.基金项目
安徽省级质量工程项目（2015sjjd008、2015jyxm191）。 （）
