An Integrated System for Detection Exudates and Severity Quantification for Diabetic Macular Edema

Abstract

The prevalence of vision loss because of Diabetic macular edema (DME), which is a complication caused by diabetes mellitus, is increasing. Seven per cent of Indian diabetic population suffer from DME or damage to macula. The study proposes an integrated model to contribute to the prevention of loss of visual acuity. For detection of exudates and visualization of severity of DME image processing, machine learning and data analysis strategies are used. Experimentation on Indian population is done using Indian Diabetic Eye Diseases Dataset (IDEDD), acquired from metropolitan region of Mumbai and publicly available Indian Diabetic Retinopathy Image Dataset (IDRiD, Nanded) and a standard public database DIARETDB1- Standard Diabetic Retinopathy Database. To negate the effect of increase in the dimensionality and overfitting of the dataset parameters, a tree-based forward search algorithm for optimal feature subset identification is proposed. This provides global insight of effect of the statistical and spatial features on model behaviour. Machine learning algorithms of random forest and support vector machine are used for classification. The sensitivity and specificity of 97.1% and 100% is achieved for IDEDD data. Severity grading is done using image processing for segmenting the vessel mesh, optic disk, and macula from the abnormal retinal scans for detecting clinically significant macular edema by visualizing exudates. The distance between optic disc—fovea (OF) and the ratio of distance (OF) to that of disc diameter is formulated for localization of macula. Region-based and distance-based grading methods for quantification of the severity and visualization are implemented. The proposed algorithm enables the building of a predictive model for primordial and secondary prevention of DME with high classification accuracy and visualization of severity grading.