Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 1, Issue 1, Pages 101-123
http://dx.doi.org/10.1260/2040-2295.1.1.101
Research Article

Semi-Automatic Anatomical Tree Matching for Landmark-Based Elastic Registration of Liver Volumes

Klaus Drechsler, Cristina Laura, Yufei Chen, and Marius Erdt

Fraunhofer Institute for Computer Graphics Research, Fraunhoferstr. 5, 64283 Darmstadt, Germany

Copyright © 2010 Hindawi Publishing Corporation. 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. T. Lange, S. Wörz, K. Rohr, and P. M. Schlag, Landmark-based 3d Elastic Registration of Pre- and Postoperative Liver CT Data, Bildverarbeitung für die Medizin (BVM), 2009.
  2. P. Hassenpflug, Gefäß basierte Registrierung zur Computer gestützten Navigation in der Leberchirurgie, Ph.D. Dissertation, Ruprecht-Karls-Universität, 2004.
  3. A. Charnoz, V. Agnus, G. Malandain, L. Soler, and M. Tajine, “Tree Matching Applied to Vascular System,” in Graph-based Representation in Pattern Recognition, Lecture Notes in Computer Science, 3434, pp. 183–192, Springer, 2005. View at Google Scholar
  4. T. Lohe, T. Kröger, S. Zidowitz, H.-O. Peitgen, and X. Jiang, “Hierarchical matching of anatomical trees for medical image registration,” in Proceedings of the First International Conference on Medical Biometrics (ICMB), Lecture Notes in Computer Science, 4901, pp. 224–231, Springer, 2008.
  5. J. H. Metzen, T. Kröger, A. Schenk, S. Zidowitz, H.-O. Peitgen, and X. Jiang, “Matching of tree Structures for Registration of Medical Images,” in Graph-Based Representations in Pattern Recognition, Springer, 2007. View at Google Scholar
  6. T. Lange, N. Papenberg, S. Heldmann et al., “3D Ultrasound-CT Registration of the Liver Using Combined Landmark-Intensity Information,” International Journal of Computer Assisted Radiology and Surgery (CARS), vol. 4, pp. 79–88, 2009. View at Google Scholar
  7. T. Heimann and H.-P. Meinzer, “Statistical Shape Models for 3D Medical Image Segmentation: A Review,” Medical Image Analysis, vol. 13, no. 4, pp. 543–563, 2009. View at Google Scholar
  8. D. Furukawa, A. Shimizu, and H. Kobatake, “Automatic Liver Segmentation based on Maximum a Posterior Probability Estimation and Level set Method,” in Proc. MICCAI Workshop 3-D Segmentat. Clinic: A Grand Challenge, pp. 117–124, 2007.
  9. S. Pan and B. M. Dawant, “Automatic 3-D Segmentation of the Liver from Abdominal CT Images: A Level-set Approach,” in Proc. SPIE Med. Imag.: Image Process, M. Sonka and K. M. Hanson, Eds., vol. 4322, pp. 128–138, 2001.
  10. R. Beichel, C. Bauer, A. Bornik, E. Sorantin, and H. Bischof, “Liver Segmentation in CT Data: A Segmentation Refinement Approach,” in Proc. MICCAI Workshop 3-D Segmentat. Clinic: A Grand Challenge, pp. 235–245, 2007.
  11. G. Schmidt, M. A. Athelogou, R. Schönmeyer, R. Korn, and G. Binnig, “Cognition Network Technology for a Fully Automated 3-D Segmentation of Liver,” in Proc. MICCAI Workshop 3-D Segmentat. Clinic: A Grand Challenge, pp. 125–133, 2007.
  12. T. Heimann et al., “Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets,” IEEE Trans Med Imaging, vol. 28, no. 8, pp. 1251–1265, 2009. View at Google Scholar
  13. C. Kirbas and F. K. H. Quek, “Vessel Extraction Techniques and Algorithms: A Survey,” in Proceedings of the third IEEE Symposium on Bioinformatics and Bioengineering, pp. 238–245, 2003.
  14. D. Selle, B. Preim, A. Schenk, and H.-O. Peitgen, “Analysis of Vasculature for Liver Surgical Planning,” IEEE Transactions on Medical Imaging, vol. 21, no. 11, pp. 1344–1357, 2002. View at Google Scholar
  15. Z. Lin, J. Jin, and H. Talbot, “Unseeded Region Growing for 3D Image Segmentation,” in Selected Papers from the Pan-Sydney Workshop on Visualisation, pp. 31–37, Australian Computer Society, Inc., 2001.
  16. D. Selle, W. Spindler, B. Preim, and H.-O. Peitgen, “Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning,” in Mathematics Unlimited - 2001 and Beyond, B. Enquist and W. Schmid, Eds., pp. 103–109, Springer, 2000. View at Google Scholar
  17. P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at Google Scholar
  18. Y. Sato, S. Nakajima, N. Shiraga et al., “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Medical Image Analysis, vol. 2, pp. 143–168, 1998. View at Google Scholar
  19. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale Vessel Enhancement Filtering,” in Medical Image Computing and Computer-Assisted Interventation (MICCAI), p. 1496, Springer, 1998. View at Google Scholar
  20. C. Bauer and H. Bischof, “A Novel Approach for Detection of Tubular Objects and its Application to Medical Image Analysis,” in Proceedings of the 30th DAGM Symposium on Pattern Recognition, Springer, 2008.
  21. H. E. Bennink, H. C. van Assen, G. J. Streekstra, R. ter Wee, J. A. E. Spaan, and B. M. ter Haar Romeny, “A Novel 3D Multi-scale Lineness Filter for Vessel Detection,” in Medical Image Computing and Computer-Assisted Interventation (MICCAI), N. Ayache, S. Ourselin, and A. J. Maeder, Eds., vol. 4792, pp. 436–443, Springer, 2007. View at Google Scholar
  22. W.-P. Choi, K.-M. Lam, and W.-C. Siu, “Extraction of the Euclidean Skeleton Based on a Connectivity Criterion,” Pattern Recognition, vol. 36, no. 3, pp. 721–729, 2003. View at Google Scholar
  23. L. Latecki, Q. Li, X. Bai, and W. Liu, “Skeletonization Using SSM of the Distance Transform,” in International Conference on Image Processing (ICIP), vol. 5, pp. 349–352, 2007.
  24. M. Ilg and R. Ogniewicz, “The Application of Voronoi Skeletons to Perceptual Grouping in Line Images,” in Proceedings of the 11th International Conference on Pattern Recognition, vol. 3, pp. 382–385, 1992.
  25. K. Palágyi, J. Tschirren, and M. Sonka, “Quantitative Analysis of Intrathoracic Airway Trees: Methods and Validation,” in Information Processing in Medical Imaging, vol. 2732, pp. 222–233, Springer, 2003. View at Google Scholar
  26. T. Lee, R. L. Kashyap, and C. Chu, “Building Skeleton Models via 3-D Medical Surface/Axis Thinning Algorithms,” in Graphical Models and Image Processing, vol. 56, pp. 462–478, Academic Press, 1994. View at Google Scholar
  27. M. Pelillo, K. Siddiqi, and S. Zucker, “Matching Hierarchical Structures Using Association Graphs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1105–1120, 1999. View at Google Scholar
  28. S. Gold and A. Rangarajan, “A Graduated Assignment Algorithm for Graph Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 4, pp. 377–388, 1996. View at Google Scholar
  29. S. Medasani, R. Krishnapuram, and Y. Choi, “Graph Matching by Relaxation of Fuzzy Assignments,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 1, pp. 173–182, 2001. View at Google Scholar
  30. A. Charnoz, V. Agnus, G. Malandain, L. Soler, and M. Tajine, “Tree Matching Applied to Vascular System,” in Graph-based Representation in Pattern Recognition, pp. 183–192, Springer, 2005. View at Google Scholar
  31. J. Tschirren, G. McLennan, K. Palagyi, E. Hoffman, and M. Sonka, “Matching and Anatomical Labeling of Human Airway Tree,” IEEE Transactions on Medical Imaging, vol. 24, no. 12, pp. 1540–1547, 2005. View at Google Scholar
  32. J. N. Kaftan, A. P. Kiraly, D. P. Naidich, and C. L. Novak, “A Novel Multipurpose Tree and Path Matching Algorithm with Application to Airway Trees,” Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 215–224, 2006.
  33. M. W. Graham and W. E. Higgins, “Globally Optimal Model-based Matching of Anatomical Trees,” in Proceedings of SPIE - Medical Imaging 2006: Image Processing, 614415, 2006.
  34. VOXEL-MAN Group, Voxel-Man Organ Atlas, University Medical Center Hamburg-Eppendorf, 2008.
  35. C. Lorenz and J. v. Berg, “A Comprehensive Shape Model of the Heart,” Medical Image Analysis, vol. 10, no. 4, pp. 657–670, 2006. View at Google Scholar
  36. R. Whitaker and X. Xue, “Variable-Conductance, Level-Set Curvature for Image Denoising,” in Proceedings of the International Conference on Image Processing, vol. 3, pp. 142–145, 2001.
  37. T.-C. Lee, R. L. Kashyap, and C.-N. Chu, “Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms,” Graphical Models and Image Process (CVGIP), vol. 56, no. 6, pp. 462–478, 1994. View at Google Scholar
  38. Y. Chen, C. O. Laura, and K. Drechsler, “Generation of a Graph Representation from Threedimensional Skeletons of the Liver Vasculature,” in Proceedings of the 2nd International Conference on BioMedical Engineering and Informatics (BMEI), 2009.