Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Hofuf, Saudi Arabia.
College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia.
Front Public Health. 2022 Mar 17;10:858327. doi: 10.3389/fpubh.2022.858327. eCollection 2022.
Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset.
从眼底图像中早期检测血管可以有效预防青光眼、眼球过度伸展和糖尿病等视网膜病变引起的永久性视网膜损伤。考虑到视网膜血管和背景的红色以及血管形态的变化，当前的血管检测方法无法分割细血管并在永久性视网膜病变主要发生的区域对其进行区分。本研究旨在提出一种新方法，结合传统的模板匹配方法和最新的深度学习（DL）解决方案。这两种方法相结合，使用 Cauchy 匹配滤波器的响应来替代眼底图像的噪声红色通道。因此，采用 U 形全连接卷积神经网络（U-net）来对像素进行端到端的血管和背景分类。将每张预处理图像划分为多个补丁，以提供足够的训练图像并加快每个实例的训练速度。已经分析了 DRIVE 公共数据库来测试所提出的方法，并测量了准确性、精确度、敏感性和特异性等指标来进行评估。评估表明，所提出模型在使用的数据集上的平均提取准确性为 0.9640。
