|Table of Contents|

Brain tumor segmentation algorithm based on multi-kernelcollaborative representation classificationGe Ting1,2,Zhan(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年05期
Page:
578-585
Research Field:
Publishing date:

Info

Title:
Brain tumor segmentation algorithm based on multi-kernelcollaborative representation classificationGe Ting1,2,Zhan
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
Keywords:
magnetic resonance images brain tumors image segmentation superpixel multi-scales multi-kernel collaborative representation classification
PACS:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2019.43.05.006
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.

References:

[1] 李娜,熊志勇,谢瑾,等. 基于Tamura纹理特征提取和SVM的多模态脑肿瘤MR图像分割[J]. 中南民族大学学报,2018,37(3):144-149.
Li Na,Xiong Zhiyong,Xie Jin,et al. Brain tumor segmentation on multi-modality magnetic resonance images based on tamura texture feature and SVM model[J]. Journal of South-Central University for Nationalities,2018,37(3):144-149.
[2]Avizenna M H,Soesanti I,Ardiyanto I. Classification of brain magnetic resonance images based on statistical texture[C]//Proceedings of the 2018 1st International Conference on Bioinformatics,Biotechnology,and Biomedical Engineering-Bioinformatics and Biomedical Engineering. Yogyakarta,Indonesia:IEEE,2018:1-5.
[3]Tong Jijun,Zhang Peng,Weng Yuxiang,et al. Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation[J]. Frontiers of Information Technology and Electronic Engineering,2018,19(4):471-480.
[4]Chen Xuan,Nguyen B P,Chui C K,et al. Reworking multilabel brain tumor segmentation:an automated framework using structured kernel sparse representation[J]. IEEE Systems,Man,and Cybernetics Magazine,2017,3(2):18-22.
[5]Lin Liang,Yang Wei,Li Chenglong,et al. Inference with collaborative model for interactive tumor segmentation in medical image sequences[J]. IEEE Transactions on Cybernetics,2016,46(12):2796-2809.
[6]Li Wei,Du Qian,Xiong Mingming. Kernel collaborative representation with Tikhonov regulari-zation for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters,2015,12(1):48-52.
[7]Huang Wei,Wang Xiaohui,Ma Yanbo,et al. Robust kernel collaborative representation for face recognition[J]. Optical Engineering,2015,54(5):053103.
[8]Ren X,Malik J. Learning a classification model for segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision. Washington DC,US:IEEE,2003:10-17.
[9]Wang Chunyao,Chen Junzhou,Li Wei. Superpixel segmentation algorithms review[J]. Journal of Application Research of Computers,2014,31(1):6-12.
[10]Achanta R,Shaji A,Smith K,et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[11]Rao Qian,Wen Hong,Yu Wen,et al. Review about superpixels and its applications[J]. Computer and Information Technology,2013,21(5):1-3.
[12]魏玉锋,梁冬泰,梁丹,等. 基于多模态信息的机器人视觉识别与定位研究[J]. 光电工程,2018,45(2):170650.
Wei Yufeng,Liang Dongtai,Liang Dan,et al. Visual identification and location algorithm for robot based on the multimodal information[J]. Opto-Electronic Engineering,2018,45(2):170650.
[13]Li Shanshan,Ni Li,Jia Xiuping,et al. Multi-scale superpixel spectralspatial classification of hyperspectral images[J]. International Journal of Remote Sensing,2016,37(20):4905-4922.
[14]Soltaninejad M,Yang Guang,Lambrou T,et al. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels[J]. Computer Methods and Programs in Biomedicine,2018,157:69-84.
[15]Liu Jianjun,Wu Zebin,Xiao Zhiyong,et al. Region-based relaxed multiple kernel collaborative representation for hyperspectral image classification[J]. IEEE Access,2017,5:20921-20933.
[16]刘纯,洪亮,陈杰,等. 融合像素—多尺度区域特征的高分辨率遥感影像分类算法[J]. 遥感学报,2015,19(2):228-239.
Liu Chun,Hong Liang,Chen Jie,et al. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image[J]. Journal of Remote Sensing,2015,19(2):228-239.
[17]James A P,Dasarathy B V. Medical image fusion:A survey of the state of the art[J]. Information Fusion,2014,19:4-19.
[18]Huang Xin,Zhang Liangpei. A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City,Northern Italy[J]. International Journal of Remote Sensing,2009,30(12):3205-3221.
[19]孙云云,江朝晖,单桂朋,等. 最优距离聚类和特征融合表达的关键帧提取[J]. 南京理工大学学报,2018,42(4):416-423.
Sun Yunyun,Jiang Zhaohui,Shan Guipeng,et al. Key frame extraction based on optimal distance clustering and feature fusion expression[J]. Journal of Nanjing University of Science and Technology,2018,42(4):416-423.
[20]陈沅涛,徐蔚鸿,吴佳英,等. 基于增量学习向量SVM方法的图像分割应用[J]. 南京理工大学学报,2014,38(1):6-11.
Chen Yuantao,Xu Weihong,Wu Jiaying,et al. Image segmentation application based on incremental learning vector SVM algorithm[J]. Journal of Nanjing University of Science and Technology,2014,38(1):6-11.
[21]Jayachandran A,Dhanasekaran R. Severity analysis of brain tumor in MRI images using modified multi-texton structure descriptor and kernel-SVM[J]. Arabian Journal for Science and Engineering,2014,39(10):7073-7086.
[22]时中荣,王胜,刘传才. 基于L2,p矩阵范数稀疏表示的图像分类方法[J]. 南京理工大学学报,2017,41(1):80-89.
Shi Zhongrong,Wang Sheng,Liu Chuancai. Sparse representation via L2,p norm for image classification[J]. Journal of Nanjing University of Science and Technology,2017,41(1):80-89.
[23]Li Jiayi,Zhang Hongyan,Zhang Liangpei,et al. Joint collaborative representation with multitask learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2014,52(9):5923-5936.
[24]李铁,张新君. 基于联合协同表示与SVM决策融合的高光谱图像分类研究[J]. 计算机应用研究,2017,34(6):1913-1916.
Li Tie,Zhang Xinjun. Research of hyperspectral image classification based on joint collaborative representation and SVM models with decision fusion[J]. Application Research of Computers,2017,34(6):1913-1916.
[25]Bi Jinbo,Zhang Tong,Bennett K P. Column-generation boosting methods for mixture of kernels[C]//Proceedings of the 10th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle,US:ACM,2004:521-526.
[26]Huang Meiyan,Yang Wei,Wu Yao,et al. Brain tumor segmentation based on local independent projection-based classification[J]. IEEE Transactions on Biomedical Engineering,2014,61(10):2633-2645.
[27]Zikic D,Glocker B,Konukoglu E,et al. Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR[C]//Proceedings of Medical Image Computing and Computer-assisted Intervention. Berlin,Germany:Springer,2012:369-376.
[28]Bauer S,Nolte L P,Reyes M. Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization[C]//Proceedings of Medical Image Computing and Computer-assisted Intervention. Berlin,Germany:Springer,2011:354-361.
[29]Zhan Tianming,Shen Fangqing,Hong Xunning,et al. A glioma segmentation method using cotraining and superpixel-based spatial and clinical constraints[J]. IEEE Access,2018,6:57113-57122.
[30]Pereira S,Pinto A,Alves V,et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging,2016,35(5):1240-1251.

Memo

Memo:
-
Last Update: 2019-11-30