There is a close correlation between retinal vascular status and physical diseases such as eye lesions. Retinal fundus images are an important basis for diagnosing diseases such as diabetes, glaucoma, hypertension, coronary heart disease, etc. Because the thickness of the retinal blood vessels is different, the minimum diameter is only one or two pixels wide, so obtaining accurate measurement results becomes critical and challenging. In this paper, we propose a new method of retinal blood vessel segmentation that is based on a multi-path convolutional neural network, which can be used for computer-based clinical medical image analysis. First, a low-frequency image characterizing the overall characteristics of the retinal blood vessel image and a high-frequency image characterizing the local detailed features are respectively obtained by using a Gaussian low-pass filter and a Gaussian high-pass filter. Then a feature extraction path is constructed for the characteristics of the low- and high-frequency images, respectively. Finally, according to the response results of the low-frequency feature extraction path and the high-frequency feature extraction path, the whole blood vessel perception and local feature information fusion coding are realized, and the final blood vessel segmentation map is obtained. The performance of this method is evaluated and tested by DRIVE and CHASE_DB1. In the experimental results of the DRIVE database, the evaluation indexes accuracy (Acc), sensitivity (SE), and specificity (SP) are 0.9580, 0.8639, and 0.9665, respectively, and the evaluation indexes Acc, SE, and SP of the CHASE_DB1 database are 0.9601, 0.8778, and 0.9680, respectively. In addition, the method proposed in this paper could effectively suppress noise, ensure continuity after blood vessel segmentation, and provide a feasible new idea for intelligent visual perception of medical images.

视网膜血管状态与身体疾病（如眼部病变）之间存在密切相关。视网膜眼底图像是诊断糖尿病，青光眼，高血压，冠心病等疾病的重要依据。由于视网膜血管的厚度不同，最小直径只有一两个像素宽，因此可获得准确的测量结果结果变得至关重要且充满挑战。在本文中，我们提出了一种基于多路径卷积神经网络的视网膜血管分割新方法，该方法可用于基于计算机的临床医学图像分析。第一，通过使用高斯低通滤波器和高斯高通滤波器分别获得表征视网膜血管图像的整体特征的低频图像和表征局部细节特征的高频图像。然后，分别针对低频图像和高频图像的特征构建特征提取路径。最后，根据低频特征提取路径和高频特征提取路径的响应结果，实现了全血管感知和局部特征信息融合编码，得到了最终的血管分割图。该方法的性能由DRIVE和CHASE_DB1评估和测试。在DRIVE数据库的实验结果中，评估指标的准确性（Acc），灵敏度（SE），和特异性（SP）分别为0.9580、0.8639和0.9665，CHASE_DB1数据库的评估指标Acc，SE和SP分别为0.9601、0.8778和0.9680。另外，本文提出的方法可以有效抑制噪声，保证血管分割后的连续性，为医学图像的智能视觉感知提供可行的新思路。