Incremental robust PCA for vessel segmentation in DSA sequences. (1st July 2022)

• Record Type: Journal Article
• Authors: Meng, Cai; Xu, Yizhou; Li, Ning; Li, Yanggang; Ren, Longfei; Xia, Kun
• Is Part Of: Biomedical physics & engineering express. Volume 8:Number 4(2022)
• Publication Date: 2022-07-01
• Subjects: vessel segmentation -- robust principal component analysis (RPCA) -- x-ray coronary angiography -- intervention surgery; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/ac682b
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: In intervention surgery, DSA images provide a new way to observe the vessels and catheters inside the patient. Extracting coronary artery from the dynamic complex background fast improves the effectiveness directly in clinical interventional surgery. This article proposes an incremental robust principal component analysis (IRPCA) method to extract contrast-filled vessels from x-ray coronary angiograms. RPCA is a matrix decomposition method that decomposes a video matrix into foreground and background, commonly used to model complex backgrounds and extract target objects. IRPCA pre-optimizes an x-ray image sequence. When a new x-ray sequence is received, IRPCA optimizes it based on the pre-optimized matrix according to the strategy of minimizing the energy function to obtain the foreground matrix of the new sequence. Besides, based on the idea that the new x-ray sequence introduces new information to the pre-optimized matrix, we propose UIRPCA to improve the performence of IRPCA. Compared with the traditional RPCA method, IRPCA and UIRPCA save much time while ensuring that other indicators remain basically unchanged. The experiment results based on real data show the superiority of the proposed method over other RPCA algorithms.