Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

• Main Authors: Shuang Song, Chenbing Du, Ying Chen, Danni Ai, Hong Song, Yong Huang, Yongtian Wang, Jian Yang
• Format: Article
• Language: English
• Published: BMC 2019-12-01
• Series: BMC Medical Informatics and Decision Making
• Subjects: X-ray angiographic image sequence; Vascular enhancement; Multi-feature; Vascular segmentation
• Online Access: https://doi.org/10.1186/s12911-019-0966-x

Abstract Background Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Methods A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. Results Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. Conclusions The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.