A retinal vessel segmentation method based on the affinity propagation clustering of superpixels was proposed herein. First, the maximum Hessian eigenvalue, the Gabor wavelet, and the B-COSFIRE filtering features were extracted from the preprocessed image to construct the three-dimensional fundus image. The fundus image was segmented into superpixel blocks, which were screened based on a pixel consistency criterion to select the best candidates; these candidates were considered as sample points and their statistical average pixel values were used as the feature vectors. Two clustering centers of the vessel and background classes were obtained by performing affinity propagation clustering on the feature space. Based on these clustering centers, the fundus pixels were classified via the nearest neighbor method for retinal vessel segmentation. The experimental results show that the accuracies are 94.63% and 94.30% for the DRIVE and STARE fundus image databases, respectively. Compared with K-means clustering, FCM (Fuzzy C-means), and other clustering methods, the proposed technique presents a high recognition degree for blood vessels and better continuity and integrity of the segmented retinal vessels.