摘要
背景:在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的:将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法:针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论:实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。
BACKGROUND： Image segmentation is a significant step of image processing and analysis. Within the traditional segmentation methods, fuzzy C means clustering （FCM） is applied widely. OBJECTIVE： To introduce point symmetry distance （PS）-FCM （PS-FCM） algorithm into the MRI brain image segmentation so as to promote the accuracy of MRI image segmentation. METHODS： In connection with the traditional FCM algorithm based on Minkowski distance, this pepper introduces PS-FCM algorithm into the MRI brain image segmentation. RESULTS AND CONCLUSION： Experimental results show that PS-FCM has obvious advantages compared with traditional FCM algorithm.
出处
《中国组织工程研究与临床康复》
CAS
CSCD
北大核心
2011年第22期4084-4086,共3页
Journal of Clinical Rehabilitative Tissue Engineering Research
