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.
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.
LU Yu-wei, HU Jun. Research on Medical Image Matching Based on SIFT and SURF Features. China Medical Devices, 2016, 31(4): 40-44.