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A Graph Optimization Problem in Virtual Colonoscopy

  • Jie Wang
  • Yaorong Ge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1449)

Abstract

This paper studies a graph optimization problem occurring in virtual colonoscopy, which concerns finding the central path of a colon model created from helical computed tomography (CT) image data. The central path is an essential aid for navigating through complex anatomy such as colon. Recently, Ge et al. [GSZ+] devised an efficient method for finding the central path of a colon. The method first generates colon data from a helical CT data volume by image segmentation. It then generates a 3D skeleton of the colon. In the ideal situation, namely, if the skeleton does not contain branches, the skeleton will be the desired central path. However, almost always the skeleton contains extra branches caused by holes in the colon model, which are artifacts produced during image segmentation. To remove false branches, we formulate a graph optimization problem and justify that the solution of the optimization problem represents the accurate central path of a colon. We then provide an efficient algorithm for solving the problem.

Keywords

Critical Path Helical Compute Tomography Central Path Virtual Colonoscopy Virtual Endoscopy 
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.

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References

  1. Blu73. H. Blum. Biological shape and visual science: part I. Journal of Theoretical Biology, 38:205–287, 1973.CrossRefGoogle Scholar
  2. GSZ+.
    Y. Ge, D. Stelts, X. Zha, J. Wang, and D. Vining. Computing the central path of colon lumen in Helical CT images. In SPIE International Symposium on Medical Imaging 1998: Image Processing (in press).Google Scholar
  3. HJR+96.
    A. Hara, C. Johnson, J. Reed, R. Ehman, and D. Ilstrup. Colorectal polyp detection with CT colography: two-versus three-dimensional techniques. Radiology, 200:49–54, 1996.Google Scholar
  4. HKW95.
    L. Hong, A. Kaufman, and Y.-C. Wei. 3D virtual colonoscopy. In Proc. 1995 IEEE Biomedical Visualization Symposium, pages 26–32, 1995.Google Scholar
  5. LKC94.
    T.-C. Lee, R. Kashyap, and C.-N. Chu. Building skeleton models via 3D medial surface/axis thinning algorithms. CVGIP: Graphical Models and Image Processing, 56:462–478, 1994.CrossRefGoogle Scholar
  6. LJK97.
    W. Lorensen, F. Jolesz, and R. Kikinis. United states patent: Virtual internal cavity inspection system. Tech. Rep. 5611025, 1997.Google Scholar
  7. SAN+96.
    R. Shahidi, V. Argiro, S. Napel, L. Gray, H. McAdams, G. Rubin, C. Beaulieu, R. Jeffrey, and A. Johnson. Assessment of several virtual endoscopy techniques using computed tomography and perspective volume rendering. In Lecture Notes in Computer Science: Visualization in Biomedical Computing, Karl Heinz Hohne and Ron Kikinis, Editors, vol. 1131, pages 521–528. Springer-Verlag, 1996.Google Scholar
  8. VSH+96.
    D. Vining, D. Stelts, G. Hunt, D. Aho, Y. Ge, and P. Hemler. Technical improvement in virtual colonoscopy. Radiology, 201:524, 1996.Google Scholar
  9. VGB94.
    D. Vining, D. Gelfand, and R. Bechtold et al. Technical feasibility of colon imaging with helical CT and virtual reality. American Journal of Roentgenology, 162:104, 1994.Google Scholar
  10. VS94.
    D. Vining and R. Shifrin. Virtual reality imaging with helical CT. American Journal of Roentgenology, 162:188, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jie Wang
    • 1
  • Yaorong Ge
    • 2
  1. 1.Department of Mathematical SciencesThe University of North Carolina at GreensboroGreensboroUSA
  2. 2.Department of Mathematics and Computer Science, and Department of Medical Engineering of the Division of Radiological Sciences, Bowman Gray School of MedicineWake Forest UniversityWinston-SalemUSA

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