The Use of Texture Features to Extract and Analyze Useful Information from Retinal Images

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.