Retinal blood vessel segmentation is one of the key components in the construction of fundus image analysis and computer-aided disease diagnosis systems. In this paper, a method of retinal blood vessel segmentation using Hessian-based orientational adaptive Gabor wavelet is proposed. According to the eigenvector of the Hessian matrix, the orientation of the blood vessel is obtained and set as the direction angle of the Gabor wavelet transform. By extracting the features of the four-scale orientational adaptive Gabor wavelet in combination with the large eigenvector of the Hessian matrix, five-dimensional retinal blood vessel features are constructed. The segmentation of the blood vessels can thus be realized through classifying the pixels of the fundus images with the support vector machine. The proposed method can accurately sense the direction of the blood vessel by only calculating the filtering response of the Gabor wavelet in this direction, which reduces the computational complexity of the feature extraction and achieves good complimentary between the large eigenvalues of the Hessian matrix and Gabor wavelet features. Experiments using the proposed method are performed on the DRIVE database to obtain better segmentation performance. The proposed method shows good performance in the extraction of small blood vessels and in the detection of vascular points at bifurcation and intersection.