Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients
Publicly available database of both fundus fluorescein fngiogram photographs and corresponding color fundus images of 30 healthy persons and 30 patients with diabetic retinopathy.
As manual analysis and diagnosis of large amount of images are time consuming, automatic detection and grading of diabetic retinopathy are desired. In this study, we use fundus fluorescein angiography and color fundus images simultaneously, extract 6 features employing curvelet transform, and feed them to support vector machine in order to determine diabetic retinopathy severity stages. These features are area of blood vessels, area, regularity of foveal avascular zone (FAZ), and the number of micro-aneurisms therein, total number of micro-aneurysms, and area of exudates. In order to extract exudates and vessels, we respectively modify curvelet coefficients of color fundus images and angiograms. The end points of extracted vessels in predefined region of interest based on optic disk are connected together to segment FAZ and can be further improved by adding several morphological operators. To extract micro-aneurysms from angiogram, first extracted vessels are subtracted from original image, and after removing detected background by morphological operators and enhancing bright small pixels, micro-aneurisms are detected. Our simulations show that the proposed system has sensitivity and specificity of 100% for grading diabetic retinopathy into 3 groups: 1) no diabetic retinopathy, 2) mild/moderate nonproliferative diabetic retinopathy, 3) severe nonproliferative/proliferative diabetic retinopathy.
Please reference the following paper if you would like to use any part of this dataset or method:
*** SH. Hajeb, H. Rabbani, MR. Akhlaghi, “Diabetic Retinopathy Grading by Digital Curvelet Transform", Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 761901, 11 pages, 2012.1607-1614, July 2012.