Patch-Based Adaptive Background Subtraction for Vascular Enhancement in X-Ray Cineangiograms

Shuang Song, Alejandro F. Frangi, Jian Yang*, Danni Ai, Chenbing Du, Yong Huang, Hong Song, Luosha Zhang, Yechen Han, Yongtian Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Objective: Automatic vascular enhancement in X-ray cineangiography is of crucial interest, for instance, for better visualizing and quantifying coronary arteries in diagnostic and interventional procedures. Methods: A novel patch-based adaptive background subtraction method (PABSM) is proposed automatically enhancing vessels in coronary X-ray cineangiography. First, pixels in the cineangiogram are described by the vesselness and Gabor features. Second, a classifier is utilized to separate the cineangiogram into the rough vascular and non-vascular region. Dilation is applied to the classified binary image to include more vascular region. Third, a patch-based background synthesis is utilized to fill the removed vascular region. Results: A database containing 320 cineangiograms of 175 patients was collected, and then an interventional cardiologist annotated all vascular structures. The performance of PABSM is compared with six state-of-the-art vascular enhancement methods regarding the precision-recall curve and C-value. The area under the precision-recall curve is .7133$, and the C-value is .9659$. Conclusion: PABSM can automatically enhance the coronary artery in the cineangiograms. It preserves the integrity of vascular topological structures, particularly in complex vascular regions, and removes noise caused by the non-uniform gray-level distribution in the cineangiogram. Significance: PABSM can avoid the motion artifacts and it eases the subsequent vascular segmentation, which is crucial for the diagnosis and interventional procedures of coronary artery diseases.

Original languageEnglish
Article number8606958
Pages (from-to)2563-2575
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number6
DOIs
Publication statusPublished - Nov 2019

Keywords

  • Learning
  • adaptive background
  • coronary cineangiography
  • enhancement

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