In-vivo high-resolution imaging of cerebral blood vessels is critical for brain functional research and clinical diagnosis. Despite well-developed magnetic resonance angiogram (MRA) techniques, a simple, robust preprocessing procedure has yet to be established. Thus, we propose a preprocessing pipeline that includes zero-fill interpolation, intensity non-uniformity correction, image denoising, vessel enhancement and segmentation. Specifically, we found that the most effective and robust denoising method is anisotropic total variation (ATV). By adopting and optimizing an improved 3D Hessian based tubular and spherical enhancement filter and a region-based level-set image segmentation method, we can automate the preprocessing of intensity-based MRAs with high fidelity.