Extracting contrast-filled vessels in X-ray angiography by graduated RPCA with motion coherency constraint

X-ray coronary angiography can provide rich dynamic information of cardiac and vascular function. Extracting contrast-filled vessel from the complex dynamic background (caused by the movement of diaphragm, lung, bones, etc.) in X-ray coronary angiograms has great clinical significance in assisting myocardial perfusion evaluation, reconstructing vessel structures for diagnosis and treatment of heart disease. Considering the angiography image sequence is a sum of a low-rank background matrix and a sparse flowing contrast agent matrix, we propose a novel graduated robust principal component analysis (RPCA) with spatio-temporal motion coherency constraint to accurately extract contrast-filled vessel from the X-ray coronary angiograms: (1) We first use a statistically structured RPCA with complex noise model exploiting the complex structural connectivity of vessel regions to identify all candidate foreground contrast-filled vessels; (2) To eliminate the background remained in the candidates, we further introduce trajectory decomposition on the candidate foregrounds to accurately extract contrast-filled vessels using motion coherency regularized RPCA, which imposes total variation minimization on the foreground trajectories to model the spatio-temporal contiguity and smoothness of the foreground trajectories. The graduated RPCA with motion coherency constraint shows to consistently outperform other state-of-the-art methods, in particular on real-world X-ray coronary angiograms that contain a significant amount of complex dynamic background motion. Experimental results on twelve sequences of real X-ray coronary angiograms are evaluated using both qualitative and quantitative methods to demonstrate the obvious advantages of our method over the state-of-the-art alternatives.