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Abnormality Detection in Eye Fundus Retina

The investigation of clinical reports suggested that more than ten percent patients with diabetes have a high risk of eye issues, since eye is a very sensitive organ in human body; it responsible for sensing information in the visible light domain, So, early detection of any abnormal features in eye fundus retinal is essential. Experts need more professional automatic detection way to detect the yellowish spots caused by diabetes; to control its propagation on the retina. The retinal image analysis is a natural approach to deal with eye diseases and other diseases that affecting eye fundus. The paper introduces a new medical diagnosis model for optic disc detection in fundus images by automated segmentation of landmarks. Automatic segmentation of retinal blood vessels from retinal images is applied. The proposed model integrates adaptive Otsu's Threshold, Hough transform and kirsch's template filtering for automated detection of candidate optic nerves. Traditional segmentation methods which are based on spatial properties of the optic disc achieve good results but usually fail to detect the optic disc in pathological images where abnormalities found, such as large exudates, are confused with the optic disc due to their similar appearance, color, and texture. A proposed segmentation model is introduced, which integrates vessels detection mechanism to achieve better sensitivity rates and high performance. Experimental results showed that the number of false responses are greatly reduced in the presence of other similar abnormal signs found in the eye fundus retina images. Experiments showed that proposed model produced high performance in detection of optic disc, blood vessels and exudates under different infection. More aware, experiments results showed high sensitivity compared with recent researches.