Robust Implementation of Foreground Extraction and Vessel Segmentation for X-ray Coronary Angiography Image Sequence

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 three-dimensional (3D) tensor input, vessel layer is regarded as a sparse tensor, and 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 dynamic interference of surrounding irrelevant tissues, the total variation (TV) regularized spatial-temporal constraint is introduced to separate the dynamic background. Subsequently, for the vessel images with uneven contrast distribution, a two-stage region growing (TSRG) method is utilized for vessel enhancement and segmentation. A global threshold segmentation is used as the preprocessing to obtain the main branch, and the Radon-Like features (RLF) filter is used to enhance and connect broken minor segments, the final vessel mask is constructed by combining the two intermediate results. The visibility of TV-TRPCA algorithm for foreground extraction and the accuracy of TSRG algorithm for vessel segmentation are evaluated on clinical XCA image sequences and third-party database. Both qualitative and quantitative results validate the superiority of the proposed method over the existing state-of-the-art approaches.