Shape context and projection geometry constrained vasculature matching for 3D reconstruction of coronary artery

Vascular structure matching in X-ray angiographic images sequence obtained at different imaging angles is important for identifying vascular structures in computer-assisted diagnosis of coronary artery diseases. In this paper, a novel shape context and projection geometry constraint method is proposed for soft matching and identification of coronary artery structures. Firstly, the matching energy function between vasculatures in different angiographic views is constructed based on the local geometry constraint and perspective projection constraint. Secondly, initial matching of the vasculatures is established by the shape context constraint. Thirdly, the deterministic annealing method is used to optimize the matching function. Hence, optimal correspondences are obtained by iteratively reducing the temperature of the optimization function. Finally, on the basis of the obtained correspondences, 3D coronary artery structure can be reconstructed according to the theory of binocular stereo vision in computer vision. Experiments show that the relationship among the different views can be accurately constructed by the proposed method. The proposed method is fully automatic, so it can assist physician to rapidly identify and correlate vascular structures from angiographic images obtained at different imaging angles.