  The extraction of contrast-filled vessels from X-ray coronary angiography
(XCA) image sequence has important clinical significance for intuitively
diagnosis and therapy. In this study, the XCA image sequence is regarded as a
3D tensor input, the vessel layer is regarded as a sparse tensor, and the
background layer is regarded as a low-rank tensor. Using tensor nuclear norm
(TNN) minimization, a novel method for vessel layer extraction based on tensor
robust principal component analysis (TRPCA) is proposed. Furthermore,
considering the irregular movement of vessels and the low-frequency dynamic
disturbance of surrounding irrelevant tissues, the total variation (TV)
regularized spatial-temporal constraint is introduced to smooth the foreground
layer. Subsequently, for vessel layer images with uneven contrast distribution,
a two-stage region growing (TSRG) method is utilized for vessel enhancement and
segmentation. A global threshold method is used as the preprocessing to obtain
main branches, and the Radon-Like features (RLF) filter is used to enhance and
connect broken minor segments, the final binary vessel mask is constructed by
combining the two intermediate results. The visibility of TV-TRPCA algorithm
for foreground extraction is evaluated on clinical XCA image sequences and
third-party dataset, which can effectively improve the performance of commonly
used vessel segmentation algorithms. Based on TV-TRPCA, the accuracy of TSRG
algorithm for vessel segmentation is further evaluated. Both qualitative and
quantitative results validate the superiority of the proposed method over
existing state-of-the-art approaches.
