Several ophthalmological and cardiovascular diseases–such as diabetic and hypertensive retinopathies, choroidal neovascularization, arteriosclerosis, among others–can be diagnosed by analyzing the structure of the retinal vasculature. Such analysis require to count with precise segmentation of blood vessels, being manual delineation tedious and time-consuming. Various algorithms for automatic blood vessel segmentation have been proposed in the last years, most based on supervised methods. These approaches deal with the automatic detection of retinal blood vessel features and non-vessel features by learning on the basis of a training set of manually segmented reference images. Performance of such methods is usually determined by the features capability of discriminating vessels from other anatomical or pathological structures. In this work, we present a review of different preprocessing and feature extraction techniques for blood vessel segmentation in retinal images. Using a linear Support Vector Machine as the segmentation approach, we study the behaviour of several state-of-the-art preprocessing and feature extraction techniques in the detection of retinal vasculature, summarizing their computation and results. Finally, we propose a standard methodology to evaluate and compare blood vessel segmentation algorithms. A publicly available data set of fundus images is employed for evaluation, and our results are compared against other state-of-the-art approaches.