Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231000061-7.doi: 10.11896/jsjkx.231000061

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Study on Automatic Segmentation Method of Retinal Blood Vessel Images

ZHAO Yanli1,2, XING Yitong2, LI Xiaomin2, SONG Cai2, WANG Peipei2   

  1. 1 School of Computer Science and Engineering,Northeastern University,Shenyang 110819,China
    2 School of Electrical Information Engineering,Ningxia Institute of Science and Technology,Shizuishan,Ningxia 753000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:ZHAO Yanli,born in 1986,doctoral candidate,lecturer.Her main research interests include computer vision and medical image-assisted diagnosis.
    XING Yitong,born in 1989,doctoral candidate,lecturer.His main research interests include deep learning and fault diagnosis.
  • Supported by:
    Natural Science Foundation of Ningxia Province(2023AAC03365).

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Abstract: With the rapid advancement of computer science and technology,digital image processing is widely used in medical diagnostics.Due to the close correlation between human health and retinal vascular characteristics,evaluating retinal images has become crucial for medical diagnoses.Traditional manual retinal vessel segmentation is time-consuming and lacks reproducibility,no longer meeting current demands.Consequently,this paper introduces an automatic retinal vessel segmentation algorithm.Firstly,it uses RGB color model,histogram equalization and morphological methods to enhance the preprocessing of the retinal image.Secondly,the Otsu threshold method is used to segment and extract the main blood vessels of the image,and then the threshold is dynamically adjusted through Gaussian matching filtering to realize the segmentation of small blood vessels,and then merge and optimize the segmented main trunk and small blood vessel images.Finally,the performance of the proposed algorithm is assessed using 20 images from the DRIVE image database,demonstrating an average accuracy,sensitivity,and specificity of 96.2%,77.3%,and 97.9%,respectively,affirming its effectiveness and reliability.

Key words: Retinal images, Vascular segmentation, Image preprocessing, OTSU threshold, Gaussian filter

CLC Number: 

  • TP391
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