Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

PATTERN RECOGNITION

卷:87

Article

Qin, Binjie¹  Jin, Mingxin¹  Hao, Dongdong¹  Lv, Yisong²  Liu, Qiegen³  Zhu, Yueqi⁴  Ding, Song⁵  Zhao, Jun¹  Fei, Baowei⁶ 

[1] Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai 200240, Peoples R China

[2] Shanghai Jiao Tong Univ, Sch Math Sci, Shanghai 200240, Peoples R China

[3] Nanchang Univ, Dept Elect Informat Engn, Nanchang 330031, Jiangxi, Peoples R China

[4] Shanghai Jiao Tong Univ, Affiliated Peoples Hosp 6, Dept Radiol, 600 Yi Shan Rd, Shanghai 200233, Peoples R China

[5] Shanghai Jiao Tong Univ, Sch Med, Ren Ji Hosp, Dept Cardiol, Shanghai 200127, Peoples R China

[6] Univ Texas Dallas, Erik Jonsson Sch Engn & Comp Sci, Dept Bioengn, Richardson, TX 75080 USA

关键词: X-ray coronary angiography;    Tensor completion;    Robust principal component analysis;    Vessel segmentation;    Layer separation;    Vessel enhancement;    Vessel recovery;   

DOI  :  10.1016/j.patcog.2018.09.015

来源: Elsevier

【 摘 要 】

This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free