Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement

Abstract

Objective: Aiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed. Methods: The multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure. Results: Experiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively. Conclusion: The segmentation accuracy is high, and better blood vessel segmentation results can be obtained. doi: https://doi.org/10.12669/pjms.37.6-WIT.4848 How to cite this:Hao Y, Xie H, Qiu R. Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement. Pak J Med Sci. 2021;37(6):1595-1599. doi: https://doi.org/10.12669/pjms.37.6-WIT.4848 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.