[1]薛晓君 张立强 薛忠 杨建华.基于水平集的三维脑部肿瘤分割[J].计算机技术与发展,2010,(12):201-204.
 XUE Xiao-jun,ZHANG Li-qiang,XUE Zhong,et al.A Brain Tumor Segmentation Method Based on Level Set[J].,2010,(12):201-204.
点击复制

基于水平集的三维脑部肿瘤分割()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2010年12期
页码:
201-204
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
A Brain Tumor Segmentation Method Based on Level Set
文章编号:
1673-629X(2010)12-0201-04
作者:
薛晓君1 张立强1 薛忠2 杨建华1
[1]西北工业大学自动化学院[2]Methodist医院研究所生物工程信息中心
Author(s):
XUE Xiao-junZHANG Li-qiangXUE ZhongYANG Jian-hua
[1]Automatic School,Northwest Polytechnic University[2]Center for Bioengineering and Informatics,Methodist Hospital Research Institute and Department ofRadiology,Methodist Hospital,Weil Cornell Medical College
关键词:
图像分割水平集脑部肿瘤核磁共振图像
Keywords:
image segmentation level set brain tumor magnetic resonance imaging
分类号:
TP39
文献标志码:
A
摘要:
脑部肿瘤的分割在临床的诊断、治疗以及研究方面都有很重要的作用,但是由于脑肿瘤的大小、类型、位置等的多变性,脑部肿瘤分割一直是一个难点问题。根据脑肿瘤在核磁共振图像上的梯度以及图像中点的强度分布提出了一种新的基于水平集的分割方法。算法定义了一个新的能量函数,更好地匹配图像中肿瘤区域的强度分布。在实际的脑部核磁共振图像上进行实验,文中算法可以准确地分割出脑部肿瘤。与传统的水平集方法比较,该算法分割结果更加准确
Abstract:
Brain tumor segmentation is a very important image processing step in diagnosis,treatment and research.But it is still a challenging task due to varying in size,shape,location,and image intensities within and around the tumor.Proposed a new brain tumor segmentation method based on the level set method using the gradient and the intensity distribution information in magnetic resonance images.A new energy function is defined to match the brain tumor intensity distribution more accurately.The proposed method is used to brain magnetic resonance images,it can segment the tumor correctly.The proposed method segments correctly compared with the traditional level set method

相似文献/References:

[1]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
 JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(12):36.
[2]张少娴 俞琼.基于时空相关性预测的运动估计的优化[J].计算机技术与发展,2010,(01):100.
 ZHANG Shao-xian,YU Qiong.An Optimization Method for Spatiotemporal Predictive Motion Estimation[J].,2010,(12):100.
[3]王兴 冯子亮.基于自适应初始值的FCM聚类图像分割[J].计算机技术与发展,2010,(03):101.
 WANG Xing,FENG Zi-liang.An Image Segmentation Algorithm Based on Adaptive Initialization FCM Clustering[J].,2010,(12):101.
[4]何小娜 逄焕利.基于二维直方图和改进蚁群聚类的图像分割[J].计算机技术与发展,2010,(03):128.
 HE Xiao-na,PANG Huan-li.Image Segmentation Based on Improved Ant Colony Clustering and Two- Dimensional Histogram[J].,2010,(12):128.
[5]宋淑娜 李金霞 胡学坤 高尚.一种自适应模糊阈值区间的图像分割方法[J].计算机技术与发展,2010,(05):121.
 SONG Shu-na,LI Jin-xia,HU Xue-kun,et al.A Method of Adaptive Fuzzy Threshold Region for Image Segmentation[J].,2010,(12):121.
[6]来磊 卢文科 邓开连.基于二维Tsallis交叉熵直线型图像阈值分割方法[J].计算机技术与发展,2010,(06):105.
 LAI Lei,LU Wen-ke,DENG Kai-lian.New Image Thresholding Segmentation Methods Based on Two-Dimensional Tsallis Cross-Entropy Liner-Type[J].,2010,(12):105.
[7]赵文琦 贾渊 彭增起.基于边缘流的水平集牛胴体眼肌图像分割[J].计算机技术与发展,2009,(04):202.
 ZHAO Wen-qi,JIA Yuan,PENG Zeng-qi.Level Set Segmentation Method Based on Edge Flow Technology Used in Rib - eye Image of Beef Carcass Segmentation[J].,2009,(12):202.
[8]黄长专 王彪 杨忠.图像分割方法研究[J].计算机技术与发展,2009,(06):76.
 HUANG Chang-zhuan,WANG Biao,YANG Zhong.A Study on Image Segmentation Techniques[J].,2009,(12):76.
[9]李光耀 聂诗良.基于小波分解和模糊聚类的图像分割方法[J].计算机技术与发展,2009,(06):121.
 LI Guang-yao,NIE Shi-liang.Image Segment Algorithm Based on Wavelet Decomposition and Fuzzy Clustering Theory[J].,2009,(12):121.
[10]李鑫环 陈立潮 赵红艳 赵勇.基于多小波分析与SOFM的MR图像分割算法研究[J].计算机技术与发展,2009,(09):104.
 LI Xin-huan,CHEN Li-chao,ZHAO Hong-yan,et al.Research on MR Image Segmentation Based on Multi- wavelet Analysis and SOFM[J].,2009,(12):104.
[11]吴亚 汪继文.水平集图像分割中重新初始化规避的探索[J].计算机技术与发展,2009,(09):69.
 WU Ya,WANG Ji-wen.Avoidance of Re- Initialization in Level Set Image Segmentation[J].,2009,(12):69.
[12]郭娟,何坤,周激流. 基于卡通提取的自然图像分割[J].计算机技术与发展,2016,26(02):12.
 GUO Juan,HE Kun,ZHOU Ji-liu. Natural Image Segmentation Based on Cartoon Component Extracting[J].,2016,26(12):12.

备注/Memo

备注/Memo:
薛晓君(1984~),男,硕士研究生,研究方向为优化算法、图像分割;杨建华,博士生导师,教授,研究方向为气体传感器、集成测试等
更新日期/Last Update: 1900-01-01