Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang, China.
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang, China.
Biomed Res Int. 2021 May 25;2021:5561125. doi: 10.1155/2021/5561125. eCollection 2021.
Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.
针对目前小视网膜血管提取不足的问题，我们提出了一种将监督学习和无监督学习算法相结合的视网膜血管分割算法。在这项研究中，我们使用了具有血管增强能力的多尺度匹配滤波器和具有编码和解码网络结构的 U-Net 模型。使用三个通道分别提取血管特征，最后合并三个通道的分割结果。本文提出的算法在 DRIVE、STARE 和 CHASE_DB1 数据集上进行了验证和评估。实验结果表明，与大多数其他方法相比，所提出的算法能够更好地分割小血管。我们得出结论，我们的算法在三个数据集的灵敏度指标上分别达到了 0.8745、0.8903 和 0.8916，比其他现有方法高近 0.1。
