? 基于特征选择的有监督视网膜血管动静脉分类
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (6) :1222-1230    DOI: 10.1007/s11390-017-1796-x
Special Section of CAD/Graphics 2017 << Previous Articles | Next Articles >>
基于特征选择的有监督视网膜血管动静脉分类
Bei-Ji Zou1,2, Member, CCF, Yao Chen1,2, Cheng-Zhang Zhu2,3,*, Member, CCF, Zai-Liang Chen1,2, Member, CCF, Zi-Qian Zhang1,2
1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 "Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Central South University Changsha 410083, China;
3 College of Literature and Journalism, Central South University, Changsha 410083, China
Supervised Vessels Classification Based on Feature Selection
Bei-Ji Zou1,2, Member, CCF, Yao Chen1,2, Cheng-Zhang Zhu2,3,*, Member, CCF, Zai-Liang Chen1,2, Member, CCF, Zi-Qian Zhang1,2
1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 "Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Central South University Changsha 410083, China;
3 College of Literature and Journalism, Central South University, Changsha 410083, China

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摘要 视网膜血管的动静脉分类对于高血压性视网膜病变和中风等心血管疾病的自动检测是很重要的。在本文中,我们提出了一种全自动的脉静脉分类(AVC)方法,重点在于对血管中心线像素的特征提取和选择。首先,在血管分割和视盘(OD)定位的后提取血管中心线。然后,在OD附近提取感兴趣区域(ROI),并且提取ROI区域内中心线像素的局部特征,灰度共生矩阵(GLCM)特征和自适应局部二进制模式(A-LBP)特征。然后,通过使用最大相关性和最小冗余(mRMR)算法进行特征选择。最后,使用特征加权k最近邻(FW-KNN)算法对动静脉血管进行分类。DRIVE数据库和INSPIRE-AVR数据库的实验结果分别达到了88.65%和88.51%的高精度。
关键词眼底图   动静脉分类   自适应局部二进制模式(A-LBP)   特征选择   特征加权的KNN算法(FW-KNN)     
Abstract: Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification (AVC) method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc (OD) localization. Then, a region of interest (ROI) is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix (GLCM) features, and an adaptive local binary patten (A-LBP) feature by using a max-relevance and min-redundancy (mRMR) scheme. Finally, a feature-weighted K-nearest neighbor (FW-KNN) algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.
Keywordsfundus image   arterial-venous classification   adaptive local binary patten (A-LBP)   feature selection   featureweighted K-nearest neighbor (FW-KNN)     
Received 2017-06-20;
本文基金:

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573380, 61702559, 61562029.

通讯作者: Cheng-Zhang Zhu     Email: anandawork@126.com
About author: Bei-Ji Zou received his B.S.degree in computer software from Zhejiang University,Hangzhou,in 1982,his M.S.and Ph.D.degrees in computer science and technology from Tsinghua University,Beijing,in 1984,and Hunan University,Changsha,in 2001,respectively.He joined the School of Computer and Communication at Hunan University,Changsha,in 1984,where he became an associate professor in 1997,and a professor in 2001.
引用本文:   
Bei-Ji Zou, Yao Chen, Cheng-Zhang Zhu, Zai-Liang Chen, Zi-Qian Zhang.基于特征选择的有监督视网膜血管动静脉分类[J]  Journal of Computer Science and Technology , 2017,V32(6): 1222-1230
Bei-Ji Zou, Yao Chen, Cheng-Zhang Zhu, Zai-Liang Chen, Zi-Qian Zhang.Supervised Vessels Classification Based on Feature Selection[J]  Journal of Computer Science and Technology, 2017,V32(6): 1222-1230
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