Diabetic macular edema （DME） is a retinal thickening involving the center of the macula. It is one of the serious eye diseases which affects the central vision and can lead to partial or even complete visual loss. T...Diabetic macular edema （DME） is a retinal thickening involving the center of the macula. It is one of the serious eye diseases which affects the central vision and can lead to partial or even complete visual loss. The only cure is timely diagnosis, prevention, and treatment of the disease. This paper presents an automated system for the diagnosis and classification of DME using color fundus image. In the proposed technique, first the optic disc is removed by applying some preprocessing steps. The preprocessed image is then passed through a classifier for segmentation of the image to detect exudates. The classifier uses dynamic thresholding technique by using some input parameters of the image. The stage classification is done on the basis of anearly treatment diabetic retinopathy study （ETDRS） given criteria to assess the severity of disease. The proposed technique gives a sensitivity, specificity, and accuracy of 98.27%, 96.58%, and 96.54%, respectively on publically available database.展开更多
文摘Diabetic macular edema （DME） is a retinal thickening involving the center of the macula. It is one of the serious eye diseases which affects the central vision and can lead to partial or even complete visual loss. The only cure is timely diagnosis, prevention, and treatment of the disease. This paper presents an automated system for the diagnosis and classification of DME using color fundus image. In the proposed technique, first the optic disc is removed by applying some preprocessing steps. The preprocessed image is then passed through a classifier for segmentation of the image to detect exudates. The classifier uses dynamic thresholding technique by using some input parameters of the image. The stage classification is done on the basis of anearly treatment diabetic retinopathy study （ETDRS） given criteria to assess the severity of disease. The proposed technique gives a sensitivity, specificity, and accuracy of 98.27%, 96.58%, and 96.54%, respectively on publically available database.
