Retinal images: Noise segmentation

In automated diagnosis of diabetic retinopathy, retinal images are used. The retinal images of poor quality need to be enhanced before the extraction of features and abnormalities. Segmentation of retinal images is essential for this purpose. The segmentation is employed to smooth and strengthen images by separating the noisy area from the overall image thus resulting in retinal image enhancement and less processing time. In this paper, we present a novel automated approach for segmentation of colored retinal images, which involves two steps. In the first step, we create binary noise segmentation mask to segment the retinal image. Second step creates final segmentation mask by applying morphological techniques. We used standard retinal image databases Diaretdb0 and Diaretdb1 to test the validation of our segmentation technique. Experimental results indicate our approach is effective and can get higher segmentation accuracy.