[1]葛 婷,詹天明,牟善祥.基于多核协同表示分类的脑肿瘤分割算法[J].南京理工大学学报(自然科学版),2019,43(05):578-585.[doi:10.14177/j.cnki.32-1397n.2019.43.05.006]
 Tianming,Mu Shanxiang.Brain tumor segmentation algorithm based on multi-kernelcollaborative representation classificationGe Ting1,2,Zhan[J].Journal of Nanjing University of Science and Technology,2019,43(05):578-585.[doi:10.14177/j.cnki.32-1397n.2019.43.05.006]
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基于多核协同表示分类的脑肿瘤分割算法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年05期
页码:
578-585
栏目:
出版日期:
2019-10-31

文章信息/Info

Title:
Brain tumor segmentation algorithm based on multi-kernelcollaborative representation classificationGe Ting1,2,Zhan
文章编号:
TP391.41
作者:
葛 婷12詹天明3牟善祥1
1.南京理工大学 电子工程与光电技术学院,江苏 南京 210094; 2.金陵科技学院 理学院,江苏 南京211169; 3.南京审计大学 信息与工程学院,江苏 南京211815
Author(s):
Tianming3Mu Shanxiang1
1.School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China; 2.School of Science,Jinling Institute of Technology,Nanjing 211169,China; 3.School of Information Engineering,Nanjing Audit University,Nanjing 211815,China
关键词:
核磁共振图像 脑肿瘤 图像分割 超像素 多尺度 多核协同表示分类
Keywords:
magnetic resonance images brain tumors image segmentation superpixel multi-scales multi-kernel collaborative representation classification
分类号:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2019.43.05.006
摘要:
为了从脑核磁共振(MR)图像中分割出脑肿瘤区域,为疾病诊断和手术导航提供参考,该文在核方法框架下提出一种基于多核协同表示分类的脑肿瘤分割算法。首先对脑肿瘤图像进行多尺度超像素分割,并构造基于超像素区域的空间特征,在多核框架中利用多核协同表示分类方法,将原始光谱信息与所提取的多尺度空间特征融合并应用于脑肿瘤图像的分类,最后结合临床特征实现了脑肿瘤区域的分割。在MICCAI BraTS 2012和2013数据集上的测试结果表明,与现有脑肿瘤分割算法相比,该文方法能够更好地提取脑肿瘤区域,并具有较好的分割精度。
Abstract:
In order to segment brain tumor regions from brain magnetic resonance(MR)image and provide reference for subsequent disease diagnosis and surgical navigation,a brain tumor segmentation algorithm is proposed based on the multi-kernel collaborative representation classification under the framework of kernel method. Firstly,multi-scale superpixel segmentations of brain tumor images are carried out and the spatial features based on superpixel regions are constructed. Then the original spectral information and the extracted multi-scale spatial features are fused by using the multi-kernel collaborative representation classification method under the multiple kernel frame work. Finally,the segmentation of brain tumor regions is realized in combination with clinical features. Test results on the data sets of MICCAI BraTS 2012 and 2013 show that,compared with the existing brain tumor segmentation algorithms,the proposed method can extract brain tumor regions better and has better segmentation accuracy.

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备注/Memo

备注/Memo:
收稿日期:2019-03-28 修回日期:2019-05-21
作者简介:葛婷(1981-),女,博士生,讲师,主要研究方向:医学图像处理与分析,E-mail:geting@jit.edu.cn。
引文格式:葛婷,詹天明,牟善祥. 基于多核协同表示分类的脑肿瘤分割算法[J]. 南京理工大学学报,2019,43(5):578-585.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-11-30