数据采集与处理2016，Vol.31Issue(5)：974-982,9.DOI:10.16337/j.1004-9037.2016.05.014
基于K-means和图割的脑部MRI分割算法
Brain MRI Segmentation Algorithm Based on K-means and Graph Cut
摘要
Abstract
To overcome the target boundary prone to be misclassification for an original image when the user-selected seed pixels become less in the graph cut algorithm.An interactive K-means and graph cut algorithm (KMGC)is proposed in the combination of the K-means with graph cut(GC)algorithm and the interactive segmentation with brain magnetic resonance image (MRI).The MRI intensity inhomogeneity is processed by K-means clustering algorithm.On this basis,the graph cut algorithm will further refine the MRI,so as to obtain effective segmentation of white matter and gray matter.We implement exten-sive segmentation experiments using both synthetic and real brain MRIs.Quantitative and qualitative an-alyses are carried out about the experimental results,and the results are compared with other segmenta-tion algorithms.The experimental results show that the KMGC algorithm can effectively divide the brain MRI,and outperform others on the segmentation effect.关键词
图割/交互式/核磁共振图像/K-均值Key words
graph cut/interactive/brain magnetic resonance image (MRI)/K-means分类
信息技术与安全科学引用本文复制引用
田换,覃晓,元昌安,刘致锦,廖剑平..基于K-means和图割的脑部MRI分割算法[J].数据采集与处理,2016,31(5):974-982,9.基金项目
国家自然科学基金(61363037)资助项目 （61363037）
南宁市邕宁区科学研究与技术开发计划(20150328A)资助项目。 （20150328A）
