Diabetic Maculopathy


Computer Aided Diagnosis (CAD) systems are very popular now days as they assist doctors in early detection of different diseases. In medical imaging, digital images are analyzed to develop such CAD systems using state of the art image processing and pattern recognition techniques. Diabetic maculopathy is one of the retinal abnormalities in which diabetic patient suffer from severe vision loss due to affected macula. It affects the central vision of the person and causes blindness in severe cases. In this research, we propose an automated medical system for the grading of diabetic maculopathy that will assist the ophthalmologists in early detection of the disease. The proposed system extracts the macula from digital retinal image using the vascular structure and optic disc location. It creates a binary map for possible exudate regions using filter banks and formulates a detailed feature vector for all regions. The system uses a Gaussian Mixture Model (GMM) based classifier to the retinal image in different stages of maculopathy by using the macula coordinates and exudate feature set. The evaluation of proposed system is performed by using publicly available standard retinal image databases. The results of our system have been compared with other methods in the literature in terms of sensitivity, specificity, positive predictive value and accuracy. Our system gives higher values as compared to others on the same databases which make it suitable for an automated medical system for grading of diabetic maculopathy.