Abstract: Fundus image analysis it is very difficult to identify the fovea region and eye diseases. Some times its cause to achieve a success to meet the cost and importance. An automated fundus image analysis system is developed for the detection of optic disc, blood vessels, fovea, etc. And also the identification of different eye diseases. The detection and analysis are proceeds in three different stages. They are extract the candidate region (preprocessing), features extraction and classification. Here, the optic disc localisation stage and a pre-processing stage to reduce noise and blood vessel structures and finally classify the fovea region. Also identifying the disease using the neural network classifier. For GF-SVM mechanisms. The optic disc localisation results in a localised point that represents the centroid of optic disc region whereas optic disc segmentation results in a complete contour of optic disc. In the optic disc localization stage, a feature vector approach that employs four salient characteristics of the optic disc is implemented. Fovea is one of the important feature of a fundus image. Fovea detection helps doctors and non-trained persons to identify Diabetic Retinopathy (DR), Age Related Macular Degeneration (AMD), Retinopathy of Pre-maturity (ROP) and some other diseases of the patients. Diabetic retinopathy is a cause of sight loss sometimes it will reach an advanced stage and cannot be cure. However, retinal image is essential and crucial for the ophthalmologists to diagnosis the disease. In the RGB image the green channel exhibits the best contrast between the vessels and background. With the help of advanced adaptive histogram equalization, thresholding method and smoothening method can detect the fovea region. Gabor filter and support vector machine are also used for classifying the features and its similar parts. The automatic screening will help for the doctors to quickly identify the condition of patients. Here, implemented a new efficient method to localize the fovea in retinal fundus image. Also, it is the new integrated efficient method to detect both disease and an eye region. In this proposed research aim for automatic screening of fovea for detection of many diseases quickly at a time. By automatically identifying the normal images, the workload and its costs will be reduced by increasing the effectiveness of the screening programs. The data base collected from Lotus Eye Hospital, Coimbatore. According to data, we can detect the sensitivity, specificity, accuracy, etc.
T. Vandarkuzhali and C.S. Ravichandran, 2016. Fovea Detection and Disease Identification Using Integreated GF-SVM Method. Asian Journal of Information Technology, 15: 2201-2209.