This letter proposes to extract contrast-filled vessels from overlapped noisy complex backgrounds in an x-ray coronary angiogram image sequence using low-rank and sparse decomposition. A refined vessel segmentation is finally achieved by implementing a radon-like feature filtering plus local-to-global adaptive thresholding to tackle the spatially varying noisy residuals in the extracted vessels. Based on real and synthetic XCA data, the experiment results demonstrate the superiority of the proposed method over the state-of-the-art methods.