Diabetic retinopathy detection in fundus image using cross sectional profiles and ANN

Abstract

An eye disease which destroys the normal vision ability of Diabetic Patients is known as diabetic retinopathy. Early diagnosis of this disease is necessary because, it is severe in the later stages. The presence of the microaneurysm (MA) is the first clear clinical symptom of this disease. MAs are red dots formed by swelling of the weak part of the capillary wall. The detection of microaneurysms in retinal fundus images is an important task for applications such as diabetic retinopathy screening and early treatment. The proposed method detects MAs by the use of directional cross sectional profiles of some central pixels. The cross sectional profiles of each local maximum pixels of the preprocessed images are drawn and then these profiles are analyzed. For each profile, peak detection step is employed and some attributes which includes the shape, height and size of the peak are computed. The numerical measures of these attributes are included in the feature set. This feature set is used as an input for the classifier. Artificial Neural Network (ANN) is used as the classifier. The results are compared with the well-known classifier Naïve Bayes and obtained good results.