A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features

The change in morphology, diameter, branching pattern and/or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports a supervised methodology for segmentation of the retinal vasculature from ocular fundus images. A 7-D feature vector is constructed by computing the outputs of morphological linear operators, line strengths and oriented Gabor filters at multiple scales. The feature vector encodes the spatial intensity measures along with vessel geometry at multiple scales. A Bayesian Classifier; the Gaussian Mixture Model is used for classification of the retinal image into vessels and non-vessel pixels. The methodology is evaluated using the images of two publicly available databases, the DRIVE database and the STARE database. Method performance on both sets of test images is better than the 2^nd human observer and other existing methodologies available in the literature.