In this study, a supervised retina blood vessel segmentation process was performed on the green channel of the RGB image using artificial neural network (ANN). The green channel is preferred because the retinal vessel structures can be distinguished most clearly from the green channel of the RGB image. The study was performed using 20 images in the DRIVE data set which is one of the most common retina data sets known. The images went through some preprocessing stages like contrastlimited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering to obtain a good segmentation. Retinal vessel structures were highlighted with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. Then, the network was trained by the binary version of the images specified as training images in the dataset and the targets are the images segmented manually by a specialist. The average segmentation accuracy for 20 images was found as 0.9492.

中文翻译：

---

使用人工神经网络对视网膜血管结构进行监督分割

在这项研究中，使用人工神经网络 (ANN) 对 RGB 图像的绿色通道进行了有监督的视网膜血管分割过程。绿色通道是首选，因为视网膜血管结构可以从 RGB 图像的绿色通道中最清楚地区分。该研究使用 DRIVE 数据集中的 20 张图像进行，这是已知的最常见的视网膜数据集之一。图像经过一些预处理阶段，如对比度限制自适应直方图均衡化 (CLAHE)、颜色强度调整、形态学操作以及中值和高斯滤波，以获得良好的分割。视网膜血管结构通过顶帽和机器人帽形态操作突出显示，并通过使用全局阈值转换为二值图像。然后，该网络由数据集中指定为训练图像的图像的二进制版本进行训练，目标是由专家手动分割的图像。20 张图像的平均分割精度为 0.9492。