Abstract: Retinal image analysis is extremely important in medical image processing. Diabetic Retinopathy (DR) is an eye disease that can lead to complete loss of visual capacity, if left undiagnosed at the initial stage. In India DR is the 3rd cause of blindness. Diabetic retinopathy is obtained automatically. A computer has used to predict the qualitative research and emerging knowledge about retina using extreme learning machine classifier. The fundus image analysis developed to assist ophthalmologists diagnosis and also functions as an automatic tool for the mass screening of diabetic. In this method, extreme learning machine is used to detect the abnormal image. Texture features are extracted by using Gray Level Co-occurrence matrix (GLCM). Fovea is one of the important feature of a fundus retinal image. During the last 30 years, people are trying to extract the different features like blood vessels, optic disk, macula, fovea automatically from retinal image. The retinal image of a person, processing and pattern recognition can be performed. It can differentiate the bright region (optic disc) and dark region (fovea). While, compares the similarity or dissimilarity between regions can detect fovea region. The architecture gets the retinal image acquired from fundus camera and pre-process the image using histogram equalization, performs the segmentation algorithm for detecting the blood vessels, optic disk and fovea.
T. Vandarkuzhali and C.S. Ravichandran, 2016. Automated Segmentation Method for Disease Identification and Fovea Detection Using GLCM and ELM. Asian Journal of Information Technology, 15: 2464-2472.