Diabetic retinopathy (DR) has become a major worldwide health problem due to the increase in blindness among diabetics at early ages. The detection of DR pathologies such as microaneurysms, hemorrhages and exudates through advanced computational techniques is of utmost importance in patient health care. New computer vision techniques are needed to improve upon traditional screening of color fundus images. The segmentation of the entire anatomical structure of the retina is a crucial phase in detecting these pathologies. This work proposes a novel framework for fast and fully automatic blood vessel segmentation and fovea detection. The preprocessing method involved both contrast limited adaptive histogram equalization and the brightness preserving dynamic fuzzy histogram equalization algorithms to enhance image contrast and eliminate noise artifacts. Afterwards, the color spaces and their intrinsic components were examined to identify the most suitable color model to reveal the foreground pixels against the entire background. Several samples were then collected and used by the renowned convexity shape prior segmentation algorithm. The proposed methodology achieved an average vasculature segmentation accuracy exceeding 96%, 95%, 98% and 94% for the DRIVE, STARE, HRF and Messidor publicly available datasets, respectively. An additional validation step reached an average accuracy of 94.30% using an in-house dataset provided by the Hospital Sant Joan of Reus (Spain). Moreover, an outstanding detection accuracy of over 98% was achieved for the foveal avascular zone. An extensive state-of-the-art comparison was also conducted. The proposed approach can thus be integrated into daily clinical practice to assist medical experts in the diagnosis of DR.

由于早期糖尿病患者的失明增加，糖尿病性视网膜病（DR）已成为世界范围内的主要健康问题。通过先进的计算技术来检测DR病理，例如微动脉瘤，出血和渗出液，对患者的医疗保健至关重要。需要新的计算机视觉技术来改进传统的彩色眼底图像筛查。视网膜整个解剖结构的分割是检测这些病理的关键阶段。这项工作为快速和全自动的血管分割和中央凹检测提出了一个新颖的框架。预处理方法包括对比度受限的自适应直方图均衡和亮度保持动态模糊直方图均衡算法，以增强图像对比度并消除噪声伪像。然后，检查色彩空间及其内在成分，以确定最合适的色彩模型，以揭示整个背景下的前景像素。然后收集了一些样品，并由著名的凸形状先验分割算法。对于DRIVE，STARE，HRF和Messidor公开可用的数据集，所提出的方法实现的平均脉管系统分割精度分别超过96％，95％，98％和94％。使用Hospital Reant（西班牙）的Sant Joan提供的内部数据集，附加的验证步骤达到94.30％的平均准确度。此外，中央凹无血管区域的检测精度达到98％以上。还进行了广泛的最新技术比较。因此，可以将提出的方法整合到日常临床实践中，以帮助医学专家诊断DR。