A novel method for detection of hard exudates from fundus images based on SVM and improved FCM

Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.

Classification of Diabetic Macular Edema and Its Stages Using Color Fundus Image

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.