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Retinal vessel segmentation using simple SPCNN model and line connector

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Abstract

The effective segmentation of retinal blood vessels is essential for the medical diagnosis of ophthalmology diseases. In this paper, a novel approach is presented to segment retinal vessels accurately and efficiently. Firstly, we propose a simple simplified pulse coupled neural network utilizing the similarity of adjacent neurons to acquire the basic structure of blood vessels. Then we apply a line connector to solve the problem of broken vessels occurring in the segmentation, in order to present a complete structure of the blood vessels and improve the accuracy of vessel identification. Experimental analyses on two publicly available databases show that the proposed methods with or without the line connector outperform the most existing methods in terms of average accuracy and have a fast response time. It is of great importance for medical diagnosis with high accuracy and short time consumption. Our methods are practicable either for retinal vessel segmentation, or for other applications of clinical research.

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Acknowledgements

The authors acknowledge the funding support from the National Natural Science Foundation of China under Grant U1701265.

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  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Linyuan Huang & Feng Liu

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  1. Linyuan Huang
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  2. Feng Liu
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Correspondence to Feng Liu.

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Huang, L., Liu, F. Retinal vessel segmentation using simple SPCNN model and line connector. Vis Comput 38, 135–148 (2022). https://doi.org/10.1007/s00371-020-02008-y

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