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.

翻译：从X射线冠状动脉造影（XCA）图像序列中提取对比剂填充的血管，对于直观诊断和治疗具有重要临床意义。本研究将XCA图像序列视为三维张量输入，血管层视为稀疏张量，背景层视为低秩张量。通过张量核范数（TNN）最小化，提出了一种基于张量鲁棒主成分分析（TRPCA）的血管层提取新方法。此外，考虑到血管的不规则运动及周围不相关组织的低频动态干扰，引入总变分（TV）正则化的时空约束对前景层进行平滑处理。随后，针对对比度分布不均的血管层图像，采用两阶段区域生长（TSRG）方法实现血管增强与分割。首先以全局阈值法作为预处理获取主要分支，再利用Radon-Like特征（RLF）滤波器增强并连接断裂的细小分支，最终结合两中间结果生成二值血管掩模。通过在临床XCA图像序列及第三方数据集上的评估，TV-TRPCA算法在前景提取中的可见性可有效提升常用血管分割算法的性能。基于TV-TRPCA，进一步评估了TSRG算法在血管分割中的精度。定性与定量结果均验证了所提方法相较于现有最先进方法的优越性。