This folder includes 25 colour fundus images of healthy persons and 35 colour fundus images of patients with diabetic retinopathy used for automatic curvelet-based detection of Foveal Avascular Zone (FAZ).
This folder includes 25 colour fundus images of healthy persons and 35 colour fundus images of patients with Diabetic Retinopathy (DR) used for automatic curvelet-based detection of Foveal Avascular Zone (FAZ).
The shape and size of the Foveal Avascular Zone (FAZ), which is responsible for central vision, can become abnormal and contribute to loss of vision in DR. In this study, appropriate features are extracted from the FAZ by means of Digital Curvelet Transform (DCUT) and used to grade of retina images into normal and abnormal classes. For this reason, DCUT is applied on enhanced color fundus images and its coefficients are modified to highlight vessels and the optic disc (OD). Through the use of this information about the anatomical location of the FAZ related to the OD and detected end points of segmented vessels, the FAZ is extracted. Then, the area and regularity of the extracted FAZ is determined and used for DR grading. This technique showed high reproducibility in characterizing the size and contour of the FAZ in diabetic maculopathy, thus it has the potential to serve as a powerful tool in the automated assessment and grading of images in a routine clinical setting.
Please reference the following paper if you would like to use any part of this dataset or method: