Supervised Vessels Classification Based on Feature Selection

被引：0
作者：
Bei-Ji Zou
Yao Chen
Cheng-Zhang Zhu
Zai-Liang Chen
Zi-Qian Zhang
机构：
[1] Central South University,School of Information Science and Engineering
[2] “Mobile Health” Ministry of Education-China Mobile Joint Laboratory,College of Literature and Journalism
[3] Central South University,undefined
[4] Central South University,undefined
来源：
Journal of Computer Science and Technology | 2017年 / 32卷
关键词：
fundus image; arterial-venous classification; adaptive local binary patten (A-LBP); feature selection; feature-weighted ; -nearest neighbor (FW-; NN);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
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页码：1222 / 1230
页数：8
相关论文
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