Identification Of Microaneurysms Research Articles

Identification of Microaneurysms and Exudates for Early Detection of Diabetic Retinopathy

Identification of Microaneurysms and Exudates for Early Detection of Diabetic Retinopathy

Comparison of Angiography Performance of Low-cost portable Fundus camera with standard FFA camera – Validation Study

Study Design: In an observational cross-sectional study, 91 eyes of 46 consecutive patients underwent simultaneous Fundus Fluorescein Angiography (FFA) for common retinal disorders. The FFA images captured by both cameras were graded by 3 Retinologists (DVS, GM, RB) in a structured format. 7 characteristics of FFA images that were used for grading were Foveal Avascular Zone (FAZ) visualization, identifying branch retinal vein, Identification of any abnormal hyper fluorescence at macula, Identification of Micro aneurysms, Identification of capillary non perfusion areas and identification of leakage from retinal neovascularization, Image quality and Impact on Clinical decision. Results were captured and statistically analyzed for interobserver and intercamera agreements.

An Effective Contrast Enhancement Method for Identification of Microaneurysms at Early Stage

ABSTRACTThe presence of microaneurysms (MAs) is usually the early sign of diabetic retinopathy (DR) disease. In this study, an effective contrast enhancement scheme has been incorporated with a diabetic screening system to improve the overall quality of MAs identification technique. The proposed scheme is compared with the existing retinal image contrast enhancement methods and a better result is established. Public datasets and also low-contrast noisy images collected from hospitals are used for MA's detection, and satisfactory results are found. The results on low-contrast noisy image diagnosis established the significance of the study. A total of 587 (training = 340, testing = 247) retinal images are examined. The mean sensitivity and specificity are scored as 95.94% and 99.21%, respectively. The value of AUC is 0.932 (SD = 0.013) from the ROC analysis.