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.