Advertisement

Automatic Localization of Balloon Markers and Guidewire in Rotational Fluoroscopy with Application to 3D Stent Reconstruction

  • Yu Wang
  • Terrence Chen
  • Peng Wang
  • Christopher Rohkohl
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

A fully automatic framework is proposed to identify consistent landmarks and wire structures in a rotational X-ray scan. In our application, we localize the balloon marker pair and the guidewire in between the marker pair on each projection angle from a rotational fluoroscopic sequence. We present an effective offline balloon marker tracking algorithm that leverages learning based detectors and employs the Viterbi algorithm to track the balloon markers in a globally optimal manner. Localizing the guidewire in between the tracked markers is formulated as tracking the middle control point of the spline fitting the guidewire. The experimental studies demonstrate that our methods achieve a marker tracking accuracy of 96.33% and a mean guidewire localization error of 0.46 mm, suggesting a great potential of our methods for clinical applications. The proposed offline marker tracking method is also successfully applied to the problem of automatic self-initialization of generic online marker trackers for 2D live fluoroscopy stream, demonstrating a success rate of 95.9% on 318 sequences. Its potential applications also include localization of landmarks in a generic rotational scan.

Keywords

Percutaneous Coronary Interven Viterbi Algorithm Marker Pair Marker Tracking Target Marker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Lu, X., Chen, T., Comaniciu, D.: Robust Discriminative Wire Structure Modeling with Application to Stent Enhancement in Fluoroscopy. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1121–1127 (2011)Google Scholar
  2. 2.
    Bismuth, V., Vaillant, R., Funck, F., Guillard, N., Najman, L.: A comprehensive study of stent visualization enhancement in X-ray images by image processing means. Medical Image Analysis 15(4), 565–576 (2011)CrossRefGoogle Scholar
  3. 3.
    Wang, P., Chen, T., Zhu, Y., Zhang, W., Zhou, S.K., Comaniciu, D.: Robust Guidewire Tracking in Fluoroscopy. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 691–698 (2009)Google Scholar
  4. 4.
    Rohkohl, C., Lauritsch, G., Hornegger, J.: Non-Periodic 3-D Motion Estimation and Reconstruction of Coronary Stents. In: Proceedings of 11th Fully 3D Meeting and 3rd HPIR Workshop (11th Int. Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine), pp. 462–465 (2011)Google Scholar
  5. 5.
    Schoonenberg, G., Lelong, P., Florent, R., Wink, O., ter Haar Romeny, B.: The Effect of Automated Marker Detection on in Vivo Volumetric Stent Reconstruction. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 87–94. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision (IJCV) 31(2), 79–116 (1998)CrossRefGoogle Scholar
  7. 7.
    Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: 10th IEEE International Conf. on Computer Vision (ICCV), pp. 1589–1596 (2005)Google Scholar
  8. 8.
    Stalder, S., Grabner, H., Van Gool, L.: Cascaded Confidence Filtering for Improved Tracking-by-Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 369–382. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Baugh, G., Kokaram, A.: A Viterbi tracker for local features. In: Proceedings of SPIE, Visual Information Processing and Communication, SPIE (2010)Google Scholar
  10. 10.
    Forney, G.D.: The Viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Sethian, J.A.: A fast marching level set for monotonically advancing fronts. Proceedings of the National Academy of Sciences 93, 1591–1595 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Cohen, L.D., Kimmel, R.: Global minimum for active contour models: a minimal path approach. International Journal of Computer Vision (IJCV) 24, 57–78 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yu Wang
    • 1
  • Terrence Chen
    • 2
  • Peng Wang
    • 2
  • Christopher Rohkohl
    • 3
  • Dorin Comaniciu
    • 2
  1. 1.Auxogyn, Inc.USA
  2. 2.Siemens CorporationCorporate Research & TechnologyPrincetonUSA
  3. 3.Siemens HealthcareForchheimGermany

Personalised recommendations