Retinal blood vessels segmentation using classical edge detection filters and the neural network

Retinal blood vessels analysis is of interest for medical screening, especially in the diagnosis of diabetic retinopathy. In this paper, we propose a new method for the segmentation of blood vessels in retinal photographs. This method is based on classical edge detection filters and artificial neural networks. Firstly, edge detection filters are applied to extract the features vector. The resulting features are used to train an artificial neural network in order to recognize each pixel as belonging to blood vessels or not. The obtained algorithm is evaluated with the publicly available DRIVE, CHASE and STARE datasets, containing retinal images frequently used for this goal. The performance of the proposed system is calculated in terms of detection accuracy, sensitivity, specificity, and the area under the ROC curve. Our model is compared to other vessel segmentation models with encouraging results obtained. The proposed algorithm is a suitable tool for automated retinal image analysis.