Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2006 (2006), Article ID 94025, 8 pages
http://dx.doi.org/10.1155/IJBI/2006/94025

Virtual Contrast for Coronary Vessels Based on Level Set Generated Subvoxel Accurate Centerlines

1Diagnostic Radiology Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892-1182, USA
2Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg 69120, Germany
3National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892-2281, USA

Received 31 January 2006; Revised 30 May 2006; Accepted 6 June 2006

Copyright © 2006 Ingmar Bitter 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. AHA, “Heart Disease and Stroke Statistics-2005 Update,” 2005, http://www.americanheart.org/.
  2. L. W. Johnson, R. Krone, M. J. Cowley et al., “Cardiac catheterization 1991: a report of the Registry of the Society for Cardiac Angiography and Interventions (SCA and I),” Catheterization and Cardiovascular Diagnosis, vol. 28, no. 3, pp. 219–220, 1993. View at Google Scholar
  3. S. Schroeder, A. Kuettner, T. Beck et al., “Usefulness of noninvasive MSCT coronary angiography as first-line imaging technique in patients with chest pain: initial clinical experience,” International Journal of Cardiology, vol. 102, no. 3, pp. 469–475, 2005. View at Publisher · View at Google Scholar
  4. C. H. McCollough, “Patient dose in cardiac computed tomography,” Herz, vol. 28, no. 1, pp. 1–6, 2003. View at Publisher · View at Google Scholar
  5. W. Y. Kim, P. G. Danias, M. Stuber et al., “Coronary magnetic resonance angiography for the detection of coronary stenoses,” New England Journal of Medicine, vol. 345, no. 26, pp. 1863–1869, 2001. View at Publisher · View at Google Scholar
  6. I. Bitter, M. Sato, M. Bender, K. T. McDonnell, A. Kaufman, and M. Wan, “CEASAR: a smooth, accurate and robust centerline extraction algorithm,” in Proceedings of the IEEE Visualization Conference, pp. 45–52, Salt Lake City, Utah, USA, October 2000.
  7. I. Bitter, A. E. Kaufman, and M. Sato, “Penalized-distance volumetric skeleton algorithm,” IEEE Transactions on Visualization and Computer Graphics, vol. 7, no. 3, pp. 195–206, 2001. View at Publisher · View at Google Scholar
  8. Y. Zhou and A. W. Toga, “Efficient skeletonization of volumetric objects,” IEEE Transactions on Visualization and Computer Graphics, vol. 5, no. 3, pp. 196–209, 1999. View at Publisher · View at Google Scholar
  9. D. Chen, B. Li, Z. Liang, M. Wan, A. Kaufman, and M. Wax, “Tree-branch searching, multiresolution approach to skeletonization for virtual endoscopy,” in Medical Imaging 2000: Image Processing, vol. 3979 of Proceedings of SPIE, pp. 726–734, San Diego, Calif, USA, February 2000.
  10. A. Telea and A. Vilanova, “A robust level-set algorithm for centerline extraction,” in Symposium on Visualization (VisSym '03), pp. 185–194, Grenoble, France, May 2003.
  11. T. Deschamps and L. D. Cohen, “Fast extraction of minimal paths in 3D images and applications to virtual endoscopy,” Medical Image Analysis, vol. 5, no. 4, pp. 281–299, 2001. View at Publisher · View at Google Scholar
  12. O. Wink, A. F. Frangi, B. Verdonck, M. A. Viergever, and W. J. Niessen, “3D MRA coronary axis determination using a minimum cost path approach,” Magnetic Resonance in Medicine, vol. 47, no. 6, pp. 1169–1175, 2002. View at Publisher · View at Google Scholar
  13. O. Wink, W. J. Niessen, and M. A. Viergever, “Multiscale vessel tracking,” IEEE Transactions on Medical Imaging, vol. 23, no. 1, pp. 130–133, 2004. View at Publisher · View at Google Scholar
  14. E. Meijering, M. Jacob, J. C. F. Sarria, and M. Unser, “A novel approach to neurite tracing in florecence microscopy images,” in Proceedings of the 5th LASTED International Conference on Signal and Image Processing, pp. 491–495, Honolulu, Hawaii, USA, August 2003.
  15. E. Meijering, M. Jacob, J. C. F. Sarria, P. Steiner, H. Hirling, and M. Unser, “Neurite tracing in fluorescence microscopy images using ridge filtering and graph searching: principles and validation,” in IEEE International Symposium on Biomedical Imaging: Macro to Nano, vol. 2, pp. 1219–1222, Arlington, Va, USA, April 2004.
  16. C. M. Van Bemmel, M. A. Viergever, and W. J. Niessen, “Semiautomatic segmentation and stenosis quantification of 3D contrast-enhanced MR angiograms of the internal carotid artery,” Magnetic Resonance in Medicine, vol. 51, no. 4, pp. 753–760, 2004. View at Publisher · View at Google Scholar
  17. A. F. Frangi, W. J. Niessen, P. J. Nederkoorn, J. Bakker, W. P. T. M. Mali, and M. A. Viergever, “Quantitative analysis of vascular morphology from 3D MR angiograms: in vitro and in vivo results,” Magnetic Resonance in Medicine, vol. 45, no. 2, pp. 311–322, 2001. View at Publisher · View at Google Scholar
  18. W. Cai, F. Dachille, and M. Meissner, “Centerline optimization using vessel quantification model,” in Medical Imaging 2005—Physiology, Function, and Structure from Medical Images, vol. 5746 of Proceedings of SPIE, no. II, pp. 796–803, San Diego, Calif, USA, February 2005.
  19. R. Van Uitert and I. Bitter, “Subvoxel Accurate Skeletons of Volumetric Data Based on Level Sets,” submitted.
  20. J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, Cambridge, UK, 1999.
  21. S. R. Aylward and E. Bullitt, “Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction,” IEEE Transactions on Medical Imaging, vol. 21, no. 2, pp. 61–75, 2002. View at Publisher · View at Google Scholar
  22. C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” Tech. Rep., Vision Interfaces and Systems Laboratory (VISLab), Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, USA, 2002. View at Google Scholar
  23. A. Huang, G. M. Nielson, A. Razdan, G. E. Farin, D. P. Baluch, and D. G. Capco, “Thin structure segmentation and visualization in three-dimensional biomedical images: a shape-based approach,” IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 1, pp. 93–102, 2006. View at Publisher · View at Google Scholar
  24. Y. Sato, C.-F. Westin, A. Bhalerao et al., “Tissue classification based on 3D local intensity structures for volume rendering,” IEEE Transactions on Visualization and Computer Graphics, vol. 6, no. 2, pp. 160–180, 2000. View at Publisher · View at Google Scholar
  25. W. Cai, F. Dachille, H. Yoshida, and G. Harris, “Fast, interactive segmentation of vessel in computed-tomographic angiography (CTA) images using selective vesselness-priority region-growing method,” in Radiological Society of North America (RSNA '05), McCormick, Chicago, November-December 2005, LPL09-06.
  26. A. Kanitsar, D. Fleischmann, R. Wegenkittl, and E. Grooller, “Diagnostic relevant visualization of vascular structures,” in IEEE Visualization, Boston, Mass, USA, October-November 2002.
  27. A. Etienne, R. M. Botnar, A. M. C. Van Muiswinkel, P. Boesiger, W. J. Manning, and M. Stuber, ““Soap-Bubble” visualization and quantitative analysis of 3D coronary magnetic resonance angiograms,” Magnetic Resonance in Medicine, vol. 48, no. 4, pp. 658–666, 2002. View at Publisher · View at Google Scholar
  28. I. Wolf, M. Hastenteufel, I. Wegner et al., “Curved reformations using the Medical Imaging Interaction Toolkit (MITK),” in Medical Imaging 2005—Visualization, vol. 5744 of Proceedings of SPIE, no. II, pp. 831–838, San Diego, Calif, USA, February 2005.