An automated system for colored retinal image background and noise segmentation

Retinal images are used for the automated diagnosis of diabetic retinopathy. The retinal image quality must be improved for the detection of features and abnormalities and for this purpose segmentation of retinal images is vital. In this paper, we present a novel automated approach for segmentation of colored retinal images. Our segmentation technique smoothes and strengthens images by separating the background and noisy area from the overall image thus resulting in retinal image enhancement and lower processing time. It contains coarse segmentation and fine segmentation. Standard retinal images databases Diaretdb0 and Diaretdb1 are used to test the validation of our segmentation technique. Experimental results indicate our approach is effective and can get higher segmentation accuracy.