Abstract
In order to quickly and accurately obtain the marker points in coronary angiography images in interventional surgery for cardiovascular diseases, this paper proposes a method for tracking the marker points in coronary angiography images. Firstly, it is necessary for the operator to manually mark a small number of vascular center points to find the position of the guide line and the labeled points on the guide wire. So the vascular center line can be obtained by using the vascular center point of artificial labeling with Hessian matrix eigenvector. Then we design a filter to detect the guide wire by combining the characteristics of the guide wire and the center line of the vessel. Finally, the marker point is detected by filtering along the guide wire. In the following images, all the vascular center points in the image are obtained by the automatic vascular centerline acquisition algorithm, and the vascular centerline of the frame is obtained by Iterative Closest Point (ICP) registration of the point set with the adjacent frame centerline. Then the guide wire acquisition method of this frame image is consistent with the above algorithm. Finally, the marker of the frame is obtained by combining the positions of the adjacent frame marker and frame guide wire. The experimental results show that our method can quickly and accurately detect the location of marker points in coronary angiography image sequence with a small amount of manual assistance, and can be applied to the computer-aided diagnosis and treatment of cardiovascular diseases.
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Bauer, C., Bischof, H.: A novel approach for detection of tubular objects and its application to medical image analysis. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 163–172. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69321-5_17
Bennink, H.E., et al.: A novel 3D multi-scale lineness filter for vessel detection. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4792, pp. 436–443. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75759-7_53
Brannigan, M.: An adaptive piecewise polynomial curve fitting procedure for data analysis. Commun. Stat. 10(18), 1823–1848 (2007)
Damon, J.: Properties of ridges and cores for two-dimensional images. J. Math. Imaging Vis. 10(2), 163–174 (1999)
Ezra, E., Sharir, M., Efrat, A.: On the ICP algorithm. In: Symposium on Computational Geometry (2006)
Fan, Z., Pei, L., Liu, X., Zhou, S.: Vascular centerline extraction of CTA images based on minimal path and Bayesian tracking. In: International Congress on Image & Signal Processing (2018)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Muliscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) International Conference on Medical Image Computing & Computer-assisted Intervention, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056195
Li, Z., Zhang, Y., Liu, G., Shao, H., Li, W., Tang, X.: A robust coronary artery identification and centerline extraction method in angiographies. Biomed. Signal Process. Control 16, 1–8 (2015)
Velho, L., Carvalho, P., Gomes, J., De Figueiredo, L.: Mathematical Optimization in Computer Graphics and Vision. Morgan Kaufmann, Burlington (2011)
Wink, O., Niessen, W.J., Viergever, M.A.: Multiscale vessel tracking. IEEE Trans. Med. Imaging 23(1), 130–133 (2004)
Xiao, R., Yang, J., Li, T., Liu, Y.: Ridge-based automatic vascular centerline tracking in X-ray angiographic images. In: Yang, J., Fang, F., Sun, C. (eds.) IScIDE 2012. LNCS, vol. 7751, pp. 793–800. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36669-7_96
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Zhang, Y. et al. (2019). Tracking of Intracavitary Instrument Markers in Coronary Angiography Images. In: Liao, H., et al. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. MLMECH CVII-STENT 2019 2019. Lecture Notes in Computer Science(), vol 11794. Springer, Cham. https://doi.org/10.1007/978-3-030-33327-0_21
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DOI: https://doi.org/10.1007/978-3-030-33327-0_21
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