Name
Title | ||
|---|---|---|
Extracting contrast-filled vessels in X-ray angiography by graduated RPCA with motion coherency constraint. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.patcog.2016.09.042 | Pattern Recognition |
Keywords | Field | DocType |
Subspace estimation,Robust principal component analysis (RPCA),Low-rank model,Spatio-temporal motion coherency,Matrix decomposition,Trajectory decomposition,X-ray coronary angiograms | Computer vision,Contiguity,Pattern recognition,Matrix (mathematics),Matrix decomposition,Robust principal component analysis,Artificial intelligence,Image sequence,X-ray Angiography,Mathematics,Trajectory,Angiography | Journal |
Volume | Issue | ISSN |
63 | 1 | 0031-3203 |
Citations | PageRank | References |
9 | 0.45 | 37 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingxin Jin | 1 | 12 | 0.81 |
| Rong Li | 2 | 15 | 1.56 |
| Jian Jiang | 3 | 9 | 1.13 |
| Binjie Qin | 4 | 50 | 7.85 |