Retinal Vessel Extraction Using Gabor Filters and Support Vector Machines

Blood vessel segmentation is the basic foundation while developing retinal screening systems, since vessels serve as one of the main retinal landmark features. This paper proposes an automated method for identification of blood vessels in color images of the retina. For every image pixel, a feature vector is computed that utilize properties of scale and orientation selective Gabor filters. The extracted features are then classified using generative Gaussian mixture model and discriminative support vector machines classifiers. Experimental results demonstrate that the area under the receiver operating characteristic (ROC) curve reached a value equal to 0.974. Moreover, it achieves 96.50% sensitivity and 97.10% specificity in terms of blood vessels identification.