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

作者：

Highlights：

• We propose a graduated RPCA with motion coherency constraint to extract contrast-filled vessels from X-ray coronary angiography.

• A statistically structured RPCA exploits the complex structural connectivity to identify all candidate vessel.

• Total variation & L1-based regularized RPCA is implemented on candidate vessels for accurate vessel detection.

• Qualitative and quantitative evaluations demonstrate the obvious advantages of our method over the state-of-the-art methods.

摘要

Highlights•We propose a graduated RPCA with motion coherency constraint to extract contrast-filled vessels from X-ray coronary angiography.•A statistically structured RPCA exploits the complex structural connectivity to identify all candidate vessel.•Total variation & L1-based regularized RPCA is implemented on candidate vessels for accurate vessel detection.•Qualitative and quantitative evaluations demonstrate the obvious advantages of our method over the state-of-the-art methods.

论文关键词：Subspace estimation,Robust principal component analysis (RPCA),Low-rank model,Spatio-temporal motion coherency,Matrix decomposition,Trajectory decomposition,X-ray coronary angiograms

论文评审过程：Received 31 January 2016, Revised 7 September 2016, Accepted 21 September 2016, Available online 6 October 2016, Version of Record 27 November 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.042