Guide-Wire Detecting Based on Speeded up Robust Features for Percutaneous Coronary Intervention

  • Prasong PusitEmail author
  • Xiaoliang XieEmail author
  • Zengguang HouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)


Percutaneous coronary intervention (PCI) is a type of endovascular surgery. In the PCI procedure, guide-wire threading under the monitoring of X-ray videos is a vital step widely used to treat narrowing stenosis of a coronary artery. Detection of guide-wire in X-ray videos is not a trivial task because guide-wire has various shapes, and the signal to noise rate is pretty low. Besides, some anatomical skeleton contours are similar to guide-wires. Therefore, it urgently needs accuracy and robust method. In this research, we present a fast and robust guide-wire detection method we offer a fast and robust guide-wire detection method, speeded up robust features (SURF) is applied to locate the tip of guide-wire in various shapes and situations. Our approach was evaluated by testing on 18 X-ray sequence images, total 1073 frames (50 frames for training and 1023 frames for testing). The detection accuracy is 92.7% with 20 fps speed that shows a promising result for guide-wires detection.


Guide-wire Signal-to-noise rate Cardiovascular diseases Percutaneous coronary intervention Guide-wire detection 


  1. 1.
    Mohr, F.W., et al.: Coronary artery bypass graft surgery versus percutaneous coronary intervention in patients with three-vessel disease and left main coronary disease: 5-year follow-up of the randomised, clinical SYNTAX trial. Lancet 381(9867), 629–638 (2013)CrossRefGoogle Scholar
  2. 2.
    Heibel, H., Glocker, B., Groher, M., Pfister, M., Navab, N.: Interventional tool tracking using discrete optimization. IEEE Trans. Med. Imaging 32(3), 544–555 (2013)CrossRefGoogle Scholar
  3. 3.
    Chen, B.J., Wu, Z., Sun, S., Zhang, D., Chen, T.: Guidewire tracking using a novel sequential segment optimization method in interventional X-ray videos. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 103–106. IEEE (2016)Google Scholar
  4. 4.
    Chang, P.L., et al.: Robust catheter and guidewire tracking using B-spline tube model and pixel-wise posteriors. IEEE Robot. Autom. Lett. 1(1), 303–308 (2016)CrossRefGoogle Scholar
  5. 5.
    Fazlali, H., et al.: Vessel region detection in coronary X-ray angiograms. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1493–1497. IEEE (2015)Google Scholar
  6. 6.
    Hernandez-Vela, A., et al.: Accurate coronary centerline extraction, caliber estimation, and catheter detection in angiographies. IEEE Trans. Inf. Technol. Biomed. 16(6), 1332–1340 (2012)CrossRefGoogle Scholar
  7. 7.
    Heibela, T.H., Glockera, B., Grohera, M., Paragios, N., Komodakis, N., Navaba, N.: Discrete tracking of parametrized curves. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1754–1761. IEEE (2009)Google Scholar
  8. 8.
    Honnorat, N., Vaillant, R., Paragios, N.: Graph-based geometric-iconic guide-wire tracking. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6891, pp. 9–16. Springer, Heidelberg (2011). Scholar
  9. 9.
    Wang, L., Xie, X.L., Gao, Z.J., Bian, G.B., Hou, Z.G.: Guide-wire detecting using a modified cascade classifier in interventional radiology. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 1240–1243. IEEE (2016)Google Scholar
  10. 10.
    Wang, L., Xie, X.L., Bian, G.B., Hou, Z.G., Cheng, X.R., Prasong, P.: Guide-wire detection using region proposal network for X-ray image-guided navigation. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3169–3175. IEEE (2017)Google Scholar
  11. 11.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  12. 12.
    Hamid, N., Yahya, A., Ahmad, R.B., Al-Qershi, O.M.: A comparison between using SIFT and SURF for characteristic region based image steganography. Int. J. Comput. Sci. Issues 9(3), 110–116 (2012)Google Scholar
  13. 13.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  14. 14.
    Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007)CrossRefGoogle Scholar
  15. 15.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). Scholar
  16. 16.
    Wasson, V., et al.: An efficient content based image retrieval based on speeded up robust features (SURF) with optimization technique. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), pp. 730–735. IEEE (2017)Google Scholar
  17. 17.
    Wu, L., Liu, B., Zhao, B.: Unsupervised change detection of remote sensing images based on SURF and SVM. In: 2017 International Conference on Computing Intelligence and Information System (CIIS), pp. 214–218. IEEE (2017)Google Scholar
  18. 18.
    Kim, Y., Jung, H.: Reconfigurable hardware architecture for faster descriptor extraction in SURF. Electron. Lett. 54, 210–212 (2018)CrossRefGoogle Scholar
  19. 19.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  20. 20.
    Feng, Z.Q., Bian, G.B., Xie, X.L., Hou, Z.G., Hao, J.L.: Design and evaluation of a bio-inspired robotic hand for percutaneous coronary intervention. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5338–5343. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, CASBeijingChina
  3. 3.CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

Personalised recommendations