摘要
为实现脑膜瘤核磁共振(MR)图像的精确分割,本文提出了一种新的基于图割的交互式图像分割算法。该方法首先提取高维图像特征,然后利用加权KNN(K-Nearest Neighbor)分类器估计待分类像素属于肿瘤与背景区域的概率,并构造新的能量函数;最后采用图割优化方法对能量函数优化求解。对脑膜瘤MR图像的分割实验表明,本方法较基于灰度信息的图割方法在精度上有明显提高。
For accurate segmentation of the magnetic resonance（MR） images of meningioma,we propose a novel interactive segmentation method based on graph cuts.The high dimensional image features was extracted,and for each pixel,the probabilities of its origin,either the tumor or the background regions,were estimated by exploiting the weighted K-nearest neighborhood classifier.Based on these probabilities,a new energy function was proposed.Finally,a graph cut optimal framework was used for the solution of the energy function.The proposed method was evaluated by application in the segmentation of MR images of meningioma,and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.
出处
《南方医科大学学报》
CAS
CSCD
北大核心
2011年第7期1164-1168,共5页
Journal of Southern Medical University
基金
国家973项目(2010CB732505)
国家自然科学青年基金(30900380)
广东省自然科学基金(9151051501000026)
福建省自然科学基金项目(2008J0312)
南京军区重点课题(08Z021)
南京军区"十一五"计划课题项目(06MA99)
广东省产学研项目(cgzhzd0717)~~
关键词
加权KNN
图割
脑膜瘤分割
weighted k-nearest neighbor
graph cuts
meningioma segmentation
