基于空间特征联合核稀疏表示的脑肿瘤提取方法

为了提高融合多序列MR图像应用于脑肿瘤提取时分割区域的准确性,基于核稀疏表示分类方法,联合多序列MR图像中的空间结构和灰度特征信息,提出一种空间特征联合的脑肿瘤核稀疏表示分类方法.首先构建各个类别的子字典,再用邻域滤波核稀疏表示方法对多序列脑MR图像进行分类,该邻域滤波核可以有效地将灰度特征与空间结构结合起来提高脑肿瘤提取的准确性.对国际数据库MICCAI Bra TS提供的临床和仿真数据进行分割.结果表明：与稀疏表示分类方法相比,所提出的基于空间特征联合核稀疏表示的脑肿瘤提取方法由于增加了空间结构信息,所得的提取准确率提高了5%~6%....

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Bibliographic Details
Published in江苏大学学报（自然科学版） Vol. 38; no. 4; pp. 449 - 454
Main Author 刘定一 刘亚军 詹天明
Format Journal Article
LanguageChinese
Published 三江学院计算机科学与工程学院,江苏南京,210012%东南大学计算机科学与工程学院,江苏南京,210000%江苏大学计算机科学与通信工程学院,江苏镇江,212013 2017
Subjects
多序列核磁共振图像
核稀疏表示
空间特征联合
脑肿瘤提取
邻域核
空间特征联合
多序列核磁共振图像
核稀疏表示
brain tumor extraction
脑肿瘤提取
neighboring filtering kernel
multi-sequence magnetic resonance image
spatial-feature combining
邻域核
kernel sparse representation
Online AccessGet full text
ISSN1671-7775
DOI10.3969/j.issn.1671-7775.2017.04.013

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Summary:为了提高融合多序列MR图像应用于脑肿瘤提取时分割区域的准确性,基于核稀疏表示分类方法,联合多序列MR图像中的空间结构和灰度特征信息,提出一种空间特征联合的脑肿瘤核稀疏表示分类方法.首先构建各个类别的子字典,再用邻域滤波核稀疏表示方法对多序列脑MR图像进行分类,该邻域滤波核可以有效地将灰度特征与空间结构结合起来提高脑肿瘤提取的准确性.对国际数据库MICCAI Bra TS提供的临床和仿真数据进行分割.结果表明：与稀疏表示分类方法相比,所提出的基于空间特征联合核稀疏表示的脑肿瘤提取方法由于增加了空间结构信息,所得的提取准确率提高了5%~6%.
Bibliography:32-1668/N
LIU DingyiI, LIU Yajun2, ZHAN Tianming3（ 1. College of Computer Science and Engineering, Sanjiang University, Nanjing, Jiangsu 210012, China; 2. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210000, China; 3. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China）
To improve the brain tumor extraction accuracy by multisequence MR image, the spatial- feature combining kernel sparse representation （KSR） was proposed to connect spatial structure information and intensity feature information for brain tumor extraction. The sub-dictionary of each label was built, and the neighboring filtering kernel based on KSR was applied to extract brain tumor from MSMR images. The spatial information and the intensity feature information were combined in the proposed method to improve the accuracy of brain tumor extraction. The clinical and simulation data from MICCAI BraTS dataset were divided by the proposed method. The resul
ISSN:1671-7775
DOI:10.3969/j.issn.1671-7775.2017.04.013