对视网膜血管形态特征的分析有助于视网膜相关疾病的诊断。为了能够更准确地分割出视网膜血管,提出一种基于双流网络的分割视网膜血管的方法。首先用具有编码器-解码器结构的卷积神经网络分别对整个血管和细血管进行分割;再将所得到的两个预测图进行融合,对融合后的图像去除伪影和噪声,得到最终的血管分割图。由于单独对细血管进行了分割,因此所提方法可以更有效地分割出一些难以识别的视网膜血管边缘和低对比度区域的细血管像素。实验结果表明,所提方法在DRIVE、STARE和CHASE_DB1三个数据库上的灵敏度分别达到0.8062、0.8308和0.8135,在性能上比其他方法更优。

The analysis of the morphological characteristics of retinal vessels is helpful in diagnosing retinal diseases. To segment retinal vessels more accurately, this paper proposes a new method based on a two-stream network. First, the whole vessel and small vessels are segmented using a convolutional neural network with an encoder-decoder structure. Subsequently, the two prediction maps are fused after the artifacts and noises are removed from the fusion image. The final vascular segmentation is then obtained. Because of the separate segmentation of small vessels, the proposed method can more effectively segment small vessel pixels that make it difficult to recognize the edges and low-contrast areas of retinal vessels. Experimental results show that the sensitivity of the proposed method on DRIVE, STARE, and CHASE_DB1 datasets is 0.8062, 0.8308, and 0.8135, respectively. The performance of the proposed method is better than that of other methods.