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

Yanjie Hao,Rong Qiu,Hongbo Xie

doi:10.12669/pjms.37.6-wit.4848

Yanjie Hao, Rong Qiu + Show 1 more

Open Access

https://doi.org/10.12669/pjms.37.6-wit.4848

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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.

Highlights

Retinal blood vessels are the only part of the human body that can be directly observed noninvasively
The ACC used in the evaluation criteria refers to the ratio of the sum of the number of vascular pixels and non-vascular pixels correctly judged to the sum of the number of vascular and non-vascular pixels in the standard image; True positive rate (TPR) refers to the number of vascular pixels correctly judged The ratio of the number to the number of all vascular pixels in the standard image; FPR refers to the ratio of the number of vascular pixels that are incorrectly determined to the number of all non-vascular pixels in the standard image, as shown in Table-I
Compared with the above two methods, the segmentation results of the STARE database by the algorithm in this study show that, the retinal blood vessels are smooth, with good connectivity, and high accuracy

Summary

Introduction

Retinal blood vessels are the only part of the human body that can be directly observed noninvasively. By detecting changes in the structure of blood vessel width, angle, and branches, it can help diagnose diseases such as diabetes, glaucoma, and hypertension.[1,2,3] The retinal vascular network is a tree-like structure with many branches, and the small blood vessels in the branches have a small contrast with the background, and the contour boundaries are blurred, which makes the automatic segmentation of small blood vessels more difficult.[4,5,6]. This paper proposes a retinal vessel segmentation method based on a multi-scale linear detector combining local enhancement and global enhancement. The multi-scale linear detector is re-divided into two parts: small scale and large scale.

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