Detection of diabetic maculopathy in human retinal images using morphological operations

Abstract

Diabetic Retinopathy (DR) is a common retinal complication associated with diabetes. Adverse changes in the retinal blood vessels lead to loss of vision without any symptoms. Diabetic retinopathy is frequent, blinding complication of the patient with high blood sugar levels that characterize diabetes. Automatic recognition of DR lesions like exudates, in digital fund us images can contribute to the diagnosis and screening of this disease. The Exudates present in the macula which is the center portion of the retina is called maculopathy or macular edema. In this approach, an automatic and efficient method to detect diabetic maculopathy which is a severe stage of diabetic retinopathy, is proposed. The real time retinal images were obtained from a nearby hospital. The retinal images were pre-processed via. Contrast Limited Adaptive Histogram Equalization (CLAHE). The preprocessed color retinal images are subjected to top hat transform and bottom hat transform. The Macula which is the darkest region was obtained. To classify the preprocessed image into exudates and non-exudates, a set of features based on color and texture were extracted. Classification was done using support Vector Machine This method appears promising as it can detect the severity of the disease.