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Supervised Vessels Classification Based on Feature Selection

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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.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Bei-Ji Zou, Yao Chen, Zai-Liang Chen & Zi-Qian Zhang

  2. “Mobile Health” Ministry of Education-China Mobile Joint Laboratory, Central South University, Changsha, 410083, China

    Bei-Ji Zou, Yao Chen, Cheng-Zhang Zhu, Zai-Liang Chen & Zi-Qian Zhang

  3. College of Literature and Journalism, Central South University, Changsha, 410083, China

    Cheng-Zhang Zhu

Authors
  1. Bei-Ji Zou
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  2. Yao Chen
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  3. Cheng-Zhang Zhu
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  4. Zai-Liang Chen
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  5. Zi-Qian Zhang
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Correspondence to Cheng-Zhang Zhu.

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Zou, BJ., Chen, Y., Zhu, CZ. et al. Supervised Vessels Classification Based on Feature Selection. J. Comput. Sci. Technol. 32, 1222–1230 (2017). https://doi.org/10.1007/s11390-017-1796-x

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  • DOI: https://doi.org/10.1007/s11390-017-1796-x

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