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


Xiaobo Zhang, Weiyang Chen*, Gang Li and Weiwei Li   Pages 1 - 6 ( 6 )


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, we propose a method to automatically identify the retinal region. The boundary of 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 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 to analyze these texture features of retinal images, and use texture features to recognize the circular retinal region automatically.


retinal image, texture feature, mask image, image contrast, retinal mask


State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medical, China Academy of Chinese Medical Sciences, Beijing 100700, College of computer science and technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, Qilu University of Technology (Shandong Academy of Sciences), Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincial Key Laboratory of Computer Networks, Jinan, College of computer science and technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353

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