We developed a contour detection based image processing algorithm based on Mamdani (Type-2) fuzzy rules for detection of blood vessels in retinal fundus images. The method uses the green channel data from eye fundus images as input, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, and median filter for background exclusion. The Mamdani (Type-2) fuzzy rules applied on image gradient value are used for edge detection. The results of experiments on the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE) and CHASEdb datasets show the applicability of the proposed method as a flexible approach which can be adapted to numerous edge detection/contour based applications. We achieved an accuracy of 0.865 for STARE dataset, an accuracy of 0.939 for the DRIVE dataset, and the accuracy of 0.950 for the ChaseDB dataset. In relation to works of other authors, our method offered a similar performance, but it offers an improved dynamics and flexibility in formulation of the linguistic threshold criteria, which can be a leading factor in design of image processing systems with dynamic and flexible rules, such as Type-2 fuzzy rules would allow, offering an interesting alternative to currently widespread deep learning applications.

我们开发了基于轮廓检测的图像处理算法，该算法基于Mamdani（类型2）模糊规则来检测视网膜眼底图像中的血管。该方法使用眼底图像的绿色通道数据作为输入，使用对比度受限的直方图均衡化（CLAHE）增强对比度，并使用中值滤波器进行背景排除。应用于图像梯度值的Mamdani（类型2）模糊规则用于边缘检测。在用于血管提取的数字视网膜图像（DRIVE），视网膜结构化分析（STARE）和CHASEdb数据集上的实验结果表明，该方法可作为一种灵活的方法适用于各种边缘检测/轮廓应用。我们对STARE数据集的精度为0.865，对于DRIVE数据集的精度为0.939，ChaseDB数据集的精度为0.950。与其他作者的作品相比，我们的方法提供了类似的性能，但是在语言阈值标准的制定方面提供了改进的动态性和灵活性，这可能是设计具有动态和灵活规则的图像处理系统的主要因素，例如正如Type-2模糊规则所允许的那样，它为当前广泛使用的深度学习应用程序提供了一种有趣的替代方法。