Robust retinal blood vessel segmentation using line detectors with multiple masks

Abstract

Several diseases can be detected and diagnosed at a very early stage by observing changes in retinal blood vessel features. In order to detect these changes, the major step required is blood vessel segmentation. In this study, an effective method for the segmentation of blood vessels on coloured digital retinal images has been proposed. The proposed method uses linear combination of line detectors at varying scales along with multiple windows of different sizes. By implementing this technique, the drawbacks encountered in multi-scale line detection such as noise and false vessel detection around the optic disk are eliminated. The performance of the proposed method is evaluated on three publicly available datasets DRIVE, STARE and CHASE by considering sensitivity, specificity, accuracy, precision, false discovery rate, F1 score, Matthews correlation coefficient and G-mean. The results were analysed by comparing with most of the other earlier existing methods and have proven to achieve higher accuracy. It has also confirmed its effectiveness and robustness with high-resolution retinal images together with better simplicity and faster implementation for reliable blood vessel segmentation.