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Retinal Image Segmentation Based on Texture Features

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1075))

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Abstract

Vessels segmentation is one of the key issues in the study of retinal image lesions. In this paper, a segmentation method of blood vessels using texture feature theory is introduced. This method is based on the texture features quantification and it is different from other blood vessel segmentation methods. We have quantified a large number of texture features that can be used in image processing. There are 41 kinds of texture features in all. The method presented in this paper is applied to retinal images in DRIVE and STARE databases. Through quantitative analysis of vascular texture features of retinal images, we found that several texture features, like gray variance, can easily identify the foreground and background of blood vessels, and thus can obtain better visual effect than the original image. It can provide more information for vessels segmentation and optic disc location.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

    Shu Zhao & Weiyang Chen

Authors
  1. Shu Zhao
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  2. Weiyang Chen
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Corresponding author

Correspondence to Weiyang Chen .

Editor information

Editors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Zhao, S., Chen, W. (2020). Retinal Image Segmentation Based on Texture Features. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_113

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