Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images

Diabetic retinopathy is a severe sight threatening disease which causes blindness among working age people. This research work presents a retinal vessel segmentation technique, which can be used in computer based retinal image analysis. This proposed method could be used as a prescreening system for the early detection of diabetic retinopathy. The algorithm implemented in this work can be effectively used for detection and analysis of vascular structures in retinal images. The retinal blood vessel morphology helps to classify the severity and identify the successive stages of a number of diseases. The changes in retinal vessel diameter are one of the symptoms for diseases based on vascular pathology. The size of typical retinal vessel is a few pixels wide and it becomes critical and challenging to obtain precise measurements using computer based automatic analysis of retinal images. This method classifies each image pixel as vessel or non-vessel and thereby produces the segmentation of vasculature in retinal images. Retinal blood vessels are identified and segmented by making use of a multilayer perceptron neural network, for which the inputs are derived from three primary colour components of the image, i.e., red, green and blue. Back propagation algorithm which provides a proficient technique to change the weights in a feed-forward network is employed. The performance of this method was evaluated and tested using the retinal images from the DRIVE database and has obtained illustrative results. The measured accuracy of the proposed system was 95.03% for the segmentation algorithm tested on this database.