About this Journal Submit a Manuscript Table of Contents
Advances in Human-Computer Interaction
Volume 2012 (2012), Article ID 632498, 9 pages
http://dx.doi.org/10.1155/2012/632498
Research Article

Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation

1Hasselt University-tUL-IBBT Expertise Centre for Digital Media, Wetenschapspark 2, 3590 Diepenbeek, Belgium
2IBBT Vision Lab, Department of Physics, University of Antwerp, Universiteitsplein 1, N.1, 2610 Wilrijk, Belgium

Received 16 August 2011; Accepted 8 November 2011

Academic Editor: Antonio Krüger

Copyright © 2012 Lode Vanacken et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Qiu and P. C. Yuen, “Interactive imaging and vision—ideas, algorithms and applications,” Pattern Recognition, vol. 43, no. 2, pp. 431–433, 2010. View at Publisher · View at Google Scholar
  2. S. D. Olabarriaga and A. W. M. Smeulders, “Interaction in the segmentation of medical images: a survey,” Medical Image Analysis, vol. 5, no. 2, pp. 127–142, 2001. View at Publisher · View at Google Scholar
  3. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, 1988. View at Publisher · View at Google Scholar
  4. T. McInerney and D. Terzopoulos, “Deformable models in medical image analysis: a survey,” Medical Image Analysis, vol. 1, no. 2, pp. 91–108, 1996.
  5. N. Spittle and A. McCluskey, “Lesson of the week: tracheal stenosis after intubation,” British Medical Journal, vol. 321, no. 7267, pp. 1000–1002, 2000.
  6. P. M. Boiselle, J. Catena, A. Ernst, and D. A. Lynch, “Tracheobronchial stenoses,” in CT of the Airways, pp. 121–149, Humana Press-Springer, 2008.
  7. R. Pinho, K. G. Tournoy, and J. Sijbers, “Assessment and stenting of tracheal stenosis using deformable shape models,” Medical Image Analysis, vol. 15, no. 2, pp. 250–266, 2011. View at Publisher · View at Google Scholar · View at PubMed
  8. J. M. Triglia, B. Nazarian, I. Sudre-Levillain, S. Marciano, G. Moulin, and A. Giovanni, “Virtual laryngotracheal endoscopy based on geometric surface modeling using spiral computed tomography data,” Annals of Otology, Rhinology and Laryngology, vol. 111, no. 1, pp. 36–43, 2002.
  9. Y. Kang, K. Engelke, and W. A. Kalender, “Interactive 3D editing tools for image segmentation,” Medical Image Analysis, vol. 8, no. 1, pp. 35–46, 2004. View at Publisher · View at Google Scholar
  10. K. McGuinness and N. E. O'Connor, “A comparative evaluation of interactive segmentation algorithms,” Pattern Recognition, vol. 43, no. 2, pp. 434–444, 2010. View at Publisher · View at Google Scholar
  11. F. Heckel, O. Konrad, H. Karl Hahn, and H.-O. Peitgen, “Interactive 3D medical image segmentation with energy-minimizing implicit functions,” Computers and Graphics, vol. 35, no. 2, pp. 275–287, 2011. View at Publisher · View at Google Scholar
  12. A. Bornik, R. Beichel, and D. Schmalstieg, “Interactive editing of segmented volumetric datasets in a hybrid 2D/3D virtual environment,” in Proceedings of the 13th ACM Symposium Virtual Reality Software and Technology (VRST '06), pp. 197–206, November 2006. View at Publisher · View at Google Scholar
  13. E. V. Zudilova-Seinstra, P. J. H. de Koning, A. Suinesiaputra et al., “Evaluation of 2D and 3D glove input applied to medical image analysis,” International Journal of Human Computer Studies, vol. 68, no. 6, pp. 355–369, 2010. View at Publisher · View at Google Scholar
  14. E. Vidholm, X. Tizon, I. Nyström, and E. Bengtsson, “Haptic guided seeding of mra images for semi-automatic segmentation,” in Proceedings of the 2nd IEEE International Symposium on Biomedical Imaging, pp. 288–291, April 2004.
  15. E. Vidholm, S. Nilsson, and I. Nyström, “Fast and robust semi-automatic liver segmentation with haptic interaction,” in Proceedings of the MICCAI, vol. 2, pp. 774–781, 2006.
  16. F. Malmberg, E. Vidholm, and I. Nyström, “A 3D live-wire segmentation method for volume images using haptic interaction,” in Discrete Geometry for Computer Imagery, vol. 4245 of Lecture Notes in Computer Science, pp. 663–673, 2006.
  17. E. Vidholm, M. Golubovic, S. Nilsson, and I. Nyström, “Accurate and reproducible semi-automatic liver segmentation using haptic interaction,” in Medical Imaging: Visualization, Image-Guided Procedures, and Modeling, vol. 6918 of Proceedings of SPIE, p. 69182Q, 2008.
  18. M. Harders and G. Székely, “Improving medical segmentation with haptic interaction,” in Proceedings of the Virtual Reality, pp. 243–250, March 2002.
  19. G. C. Burdea, Force and Touch Feedback for Virtual Reality, Wiley Interscience, 1996.
  20. D. C. Ruspini, K. Kolarov, and O. Khatib, “The haptic display of complex graphical environments,” in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 345–352, 1997.
  21. A. Kulik, J. Hochstrate, A. Kunert, and B Froehlich., “The influence of input device characteristics on spatial perception in desktop-based 3D applications,” in Proceedings of the Symposium on 3D User Interfaces, pp. 59–66, 2009.
  22. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945.
  23. P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006.