Diabetic Macular Edema Detection and Severity Grading

The aim of this paper is to apply Imaging science to visualize foreign bodies' like bright lesions, exudates and characterize them with a view to using them productively to examine and diagnose the severity of Diabetic Macular edema (DME) in human eyes. For the recognition of DME, a systemic flow is proposed. The methodology consists of using Contrast-Limiting Adaptive Histogram Equalization (CLAHE) and Alternate sequential filtering (AFS) techniques to obtain blood vessel mesh. The anatomical structures like optic disc are masked and the macular region is found for severity grading. Thresholding is applied to preserve the necessary details from the grayscale image. This image is processed with the edge detected image to obtain the exudates' presence. Finally, the severity grading is done on retinal images to obtain bright lesions. The accuracy of the algorithm was found to be 89.9 percent.