Sun R, Shi J, Yang X (2018) Tensor Voting Based Vascular Bifurcation Detection in CT Images of Lung. Int J Radiol Imaging Technol 4:040.


© 2018 Sun R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

RESEARCH ARTICLE | OPEN ACCESS DOI: 10.23937/2572-3235.1510040

Tensor Voting Based Vascular Bifurcation Detection in CT Images of Lung

Ruifang Sun1, Jingli Shi1 and Xuan Yang1*

College of Computer Science and Software Engineering, Shenzhen University, Guangdong, China


The accurate detection of vascular bifurcations is not helpful for pulmonary vascular disease diagnosis, but vital in (Computed Tomography) CT image analysis and processing of lung. We propose a tensor voting based method for vascular bifurcation detection in CT image of lung, which a vessel enhancement method is firstly proceeding to initially extract vessel structure, on which we perform ball voting with the pixels. Then counting the number of votes of each pixel and calculating the local maximum value of ball tensor salience of pixel. All the points with local maximum are as original candidate bifurcations. Finally, Principal Component Analysis (PCA) algorithm is utilized to eliminate non-bifurcations to get accurate bifurcations. To our best knowledge, there is no such application for tensor voting in this area. Experiment results of synthetic data and CT images of lung show that our method has better detection ratio, lower false detection ratio and better robustness than other vascular bifurcation detection methods.