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.

中文翻译：

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使用简单的 SPCNN 模型和线连接器进行视网膜血管分割

视网膜血管的有效分割对于眼科疾病的医学诊断至关重要。在本文中，提出了一种准确有效地分割视网膜血管的新方法。首先，我们提出了一个简单的简化脉冲耦合神经网络，利用相邻神经元的相似性来获取血管的基本结构。然后我们应用线连接器来解决分割中出现的血管断裂问题，以呈现血管的完整结构并提高血管识别的准确性。对两个公开可用数据库的实验分析表明，无论有没有线路连接器，所提出的方法在平均准确度方面都优于大多数现有方法，并且具有快速的响应时间。它对于准确率高、耗时短的医学诊断具有重要意义。我们的方法既适用于视网膜血管分割，也适用于临床研究的其他应用。