Due to the complex morphology and characteristic of retinal vessels, it remains challenging for most of the existing algorithms to accurately detect them. This paper proposes a supervised retinal vessels extraction scheme using constrained-based nonnegative matrix factorization (NMF) and three dimensional (3D) modified attention U-Net architecture. The proposed method detects the retinal vessels by three major steps. First, we perform Gaussian filter and gamma correction on the green channel of retinal images to suppress background noise and adjust the contrast of images. Then, the study develops a new within-class and between-class constrained NMF algorithm to extract neighborhood feature information of every pixel and reduce feature data dimension. By using these constraints, the method can effectively gather similar features within-class and discriminate features between-class to improve feature description ability for each pixel. Next, this study formulates segmentation task as a classification problem and solves it with a more contributing 3D modified attention U-Net as a two-label classifier for reducing computational cost. This proposed network contains an upsampling to raise image resolution before encoding and revert image to its original size with a downsampling after three max-pooling layers. Besides, the attention gate (AG) set in these layers contributes to more accurate segmentation by maintaining details while suppressing noises. Finally, the experimental results on three publicly available datasets DRIVE, STARE, and HRF demonstrate better performance than most existing methods.

由于视网膜血管的形态和特征复杂，对于大多数现有算法来说，准确检测它们仍然具有挑战性。本文提出了一种基于约束的非负矩阵分解（NMF）和三维（3D）修改注意力U-Net架构的有监督的视网膜血管提取方案。所提出的方法通过三个主要步骤来检测视网膜血管。首先，我们对视网膜图像的绿色通道执行高斯滤波和伽马校正，以抑制背景噪声并调整图像的对比度。然后，研究开发了一种新的类内和类间约束NMF算法，以提取每个像素的邻域特征信息并减小特征数据的维数。通过使用这些约束，该方法可以有效地收集类内相似特征并区分类间特征，以提高每个像素的特征描述能力。接下来，本研究将分割任务表述为分类问题，并使用更具贡献的3D修改注意力U-Net作为两标签分类器来解决，以降低计算成本。该提议的网络包含一个上采样，以在编码之前提高图像分辨率，并在三个最大池化层之后通过下采样将图像恢复为其原始大小。此外，在这些层中设置的注意门（AG）通过在保持细节的同时抑制噪声的同时，有助于更准确地进行分割。最后，在三个可公开获得的数据集DRIVE，STARE和HRF上的实验结果证明，其性能优于大多数现有方法。