Construction of a dynamic network for retinal vessel segmentation based on computer vision

Article type: Research Article

Authors: Zhang, Runze

Affiliations: Faculty of Data Science, City University of Macau, Macau 999078, China | E-mail: 18256716921@163.com

Correspondence: [*] Corresponding author: Faculty of Data Science, City University of Macau, Macau 999078, China. E-mail: 18256716921@163.com

Abstract: This paper is focused on the field of computer vision in order to investigate the presentation properties of retinal blood vessels. Combining the structure of convolutional neural networks, activation functions, and common metrics in semantic segmentation, a dynamic network model for retinal vessel segmentation based on computer vision is constructed. The purpose of this paper is to discuss the results of retinal vascular segmentation based on computer vision. The image connection and alignment pattern selection process is also established to match retinal vessel images by computer vision. The performance of the dynamic network constructed here and the results of retinal vessel segmentation were then analyzed in three publicly available datasets, DRIVE (digital retinal images for vessel extraction), CHASE_DB1, and STARE (structured snalysis of the retinal. The ROC (retinopathy online challenge) curves on all three datasets exceeded 0.9, showing high performance, and the area under the PR curve exceeded 0.88. The accuracy of the results for retinal vessel segmentation was around 96%. Based on the semantic segmentation direction in the field of computer vision in this study, the dynamic network for retinal vessel segmentation can be well constructed.

Keywords: Convolutional neural network, computer vision, open dataset, public dataset, retinal vasculature, semantic segmentation

DOI: 10.3233/JCM-237110

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 6, pp. 3375-3389, 2023