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Retinal Vessel Segmentation Using Parallel Grayscale Skeletonization Algorithm and Mathematical Morphology

Retinal vessel segmentation is an important step for the detection of numerous system diseases, such as glaucoma, diabetic retinopathy, and others. Thus, the retinal blood vessel analysis can be used to diagnose and to monitor the progress of these diseases. Manual segmentation of fundus images is a long and tedious task that requires a specialist. Therefore, many algorithms have been developed for this purpose. This paper demonstrates an automated method for retinal blood vessel segmentation based on the combination of topological and morphological vessel extractors. Each of these extractors is based on different blood vessel features to increase the detection robustness. The final segmentation is obtained intersecting the two resulting images, smoothing the vessel borders and removing spurious objects remaining. Our proposed method is tested on DRIVE and STARE databases, achieving an average accuracy of 0.9565 and 0.9568, respectively, with good values of sensitivity and specificity.