An improved algorithm for vessel centerline tracking in coronary angiograms

Authors:
Yan Xu;Hui Zhang;Hao Li;Guangshu Hu
Affiliations:
Department of Biomedical Engineering, Tsinghua University, China;Department of Biomedical Engineering, Tsinghua University, China;Department of Biomedical Engineering, Tsinghua University, China;Department of Biomedical Engineering, Tsinghua University, China
Venue:
Computer Methods and Programs in Biomedicine
Year:
2007

Citing 6
Cited 2

Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
Muliscale Vessel Enhancement Filtering

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation

SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images

CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Digital Image Processing Using MATLAB

Digital Image Processing Using MATLAB
A review of vessel extraction techniques and algorithms

ACM Computing Surveys (CSUR)

Retinal vessel segmentation using a probabilistic tracking method

Pattern Recognition
A simple framework to generate 3D patient-specific model of coronary artery bifurcation from single-plane angiographic images

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

For automated visualization and quantification of artery diseases, the accurate determination of the arterial centerline is a prerequisite. Existing tracking-based approaches usually suffer from the inaccuracy, inflexion and discontinuity in the extracted centerlines, and they may even fail in complicated situations. In this paper, an improved algorithm for coronary arterial centerline extraction is proposed, which incorporates a new tracking direction updating scheme, a self-adaptive magnitude of linear extrapolation and a dynamic-size search window for matched filtering. A simulation study is conducted for the determination of the optimal weighting factor which is used to combine the geometrical topology information and intensity distribution information to obtain the proposed tracking direction. Synthetic and clinical examples, representing some difficult situations that may occur in coronary angiograms, are presented. Results show that the proposed algorithm outperforms the conventional methods. By adopting the proposed algorithm, centerlines are successfully extracted under these complicated situations, and with satisfactory accuracy.