计算机工程2011，Vol.37Issue(5)：232-234,3.
基于K均值聚类的图割医学图像分割算法
Graph Cuts Medical Image Segmentation Algorithm Based on K-means Clustering
摘要
Abstract
Graph cuts is an interactive segmentation algorithm based on boundary and region properties of objects in images.The region term in conventional graph cuts is based on Gaussian Mixture Model(GMM).However, it is not only a slow process, but sometimes it can't describe the distribution of pixels in objects precisely.This paper proposes an improved algorithm based on K-means clustering graph cuts.Its evaluation is performed using both phantoms and real Magnetic Resonance Imaging(MRI) of brain, the effectiveness and efficiency of the proposed algorithm are showed.And in particular, an accurate and robust results in segmenting images with noise and intensity non-uniformity with a low computational cost can be achieved.关键词
图像分割/图割/K均值聚类/脑部核磁共振图像Key words
image segmentation/ graph cuts/ K-means clustering/ Magnetic Resonance Imaging(MRI) of brain分类
信息技术与安全科学引用本文复制引用
吴永芳,杨鑫,徐敏,张星..基于K均值聚类的图割医学图像分割算法[J].计算机工程,2011,37(5):232-234,3.基金项目
国家自然科学基金资助项目(60621001) （60621001）
中国科学院知识创新工程重要方向基金资助项目"计算机辅助肝脏手术前风险定量分析预测及术后功能评估系统"(KSCX2-YW-R-262) （KSCX2-YW-R-262）
