Background: The analysis of retinal images can help to detect retinal abnormalities that are caused by cardiovascular and retinal disorders.

Objective: In this paper, we propose methods based on texture features for mining and analyzing information from retinal images.

Methods: The recognition of the retinal mask region is a prerequisite for retinal image processing. However, there is no way to automatically recognize the retinal region. By quantifying and analyzing texture features, a method is proposed to automatically identify the retinal region. The boundary of the circular retinal region is detected based on the image texture contrast feature, followed by the filling of the closed circular area, and then the detected circular retinal mask region can be obtained.

Results: The experimental results show that the method based on the image contrast feature can be used to detect the retinal region automatically. The average accuracy of retinal mask region detection of images from the Digital Retinal Images for Vessel Extraction (DRIVE) database was 99.34%.

Conclusion: This is the first time these texture features of retinal images are analyzed, and texture features are used to recognize the circular retinal region automatically.

背景：视网膜图像的分析可以帮助检测由心血管和视网膜疾病引起的视网膜异常。

目的：在本文中，我们提出了一种基于纹理特征的方法来挖掘和分析视网膜图像中的信息。

方法：识别视网膜掩模区域是视网膜图像处理的先决条件。但是，无法自动识别视网膜区域。通过量化和分析纹理特征，提出了一种自动识别视网膜区域的方法。基于图像纹理对比度特征，检测出圆形视网膜区域的边界，然后填充闭合的圆形区域，从而可以获得检测到的圆形视网膜掩模区域。

结果：实验结果表明，基于图像对比度特征的方法可用于自动检测视网膜区域。来自用于血管提取的数字视网膜图像（DRIVE）数据库的图像的视网膜掩模区域检测的平均准确性为99.34％。

结论：这是第一次分析这些视网膜图像的纹理特征，并使用纹理特征自动识别圆形视网膜区域。