Advertisement

Avoiding Mesh Folding in 3D Optimal Surface Segmentation

  • Christian Bauer
  • Shanhui Sun
  • Reinhard Beichel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

The segmentation of 3D medical images is a challenging problem that benefits from incorporation of prior shape information. Optimal Surface Segmentation (OSS) has been introduced as a powerful and flexible framework that allows segmenting the surface of an object based on a rough initial prior with robustness against local minima. When applied to general 3D meshes, conventional search profiles constructed for the OSS may overlap resulting in defective segmentation results due to mesh folding. To avoid this problem, we propose to use the Gradient Vector Flow field to guide the construction of non-overlapping search profiles. As shown in our evaluation on segmenting lung surfaces, this effectively solves the mesh folding problem and decreases the average absolute surface distance error from 0.82±0.29 mm (mean±standard deviation) to 0.79±0.24 mm.

Keywords

Segmentation Result Initial Vector Active Shape Model Gradient Vector Flow Surface Normal Direction 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal surface segmentation in volumetric Images-A Graph-Theoretic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 119–134 (2006)CrossRefGoogle Scholar
  2. 2.
    Yin, Y., Zhang, X., Williams, R., Wu, X., Anderson, D.D., Sonka, M.: LOGISMOS–layered optimal graph image segmentation of multiple objects and surfaces: carti- lage segmentation in the knee joint. IEEE Transactions on Medical Imaging 29, 2023–2037 (2010)CrossRefGoogle Scholar
  3. 3.
    Lee, K., Johnson, R.K., Yin, Y., Wahle, A., Olszewski, M.E., Scholz, T.D., Sonka, M.: Three-dimensional thrombus segmentation in abdominal aortic aneurysms using graph search based on a triangular mesh. Computers in Biology and Medicine 40, 271–278 (2010)CrossRefGoogle Scholar
  4. 4.
    Garvin, M.K., Abramoff, M.D., Kardon, R., Russell, S.R., Wu, X., Sonka, M.: Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE Transactions on Medical Imaging 27, 1495–1505 (2008) PMID: 18815101CrossRefGoogle Scholar
  5. 5.
    Li, K., Jolly, M.: Simultaneous detection of multiple elastic surfaces with application to tumor segmentation in CT images. In: Proceedings of SPIE, San Diego, CA, USA, pp. 69143S–69143S–11 (2008)Google Scholar
  6. 6.
    Sun, S., McLennan, G., Hoffman, E.A., Beichel, R.: Model-based segmentation of pathological lungs in volumetric ct data. In: Proc. of Third International Workshop on Pulmonary Image Analysis, pp. 31–40 (2010)Google Scholar
  7. 7.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of Min-Cut/Max-Flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004) ACM ID: 1018355CrossRefzbMATHGoogle Scholar
  8. 8.
    Song, Q., Wu, X., Liu, Y., Smith, M., Buatti, J., Sonka, M.: Optimal graph search segmentation using Arc-Weighted graph for simultaneous surface detection of bladder and prostate. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 827–835. Springer, Heidelberg (2009) ACM ID: 1691283CrossRefGoogle Scholar
  9. 9.
    Yin, Y., Zhang, X., Sonka, M.: Optimal multi-object multi-surface graph search segmentation: Full-joint cartilage delineation in 3d. In: Medical Image Understanding and Analysis, pp. 104–108 (2008)Google Scholar
  10. 10.
    Yin, Y., Song, Q., Sonka, M.: Electric field theory motivated graph construction for optimal medical image segmentation. In: Torsello, A., Escolano, F., Brun, L. (eds.) GbRPR 2009. LNCS, vol. 5534, pp. 334–342. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Yin, Y.: Multi-surface, multi-object optimal image segmentation: application in 3D knee joint imaged by MR. PhD thesis, The University of Iowa (2010)Google Scholar
  12. 12.
    Hassouna, M.S., Farag, A.A.: Variational curve skeletons using gradient vector ow. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2257–2274 (2009)CrossRefGoogle Scholar
  13. 13.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector ow. IEEE Transactions on Image Processing 7, 359–369 (1998)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Bauer
    • 1
    • 2
  • Shanhui Sun
    • 1
    • 2
  • Reinhard Beichel
    • 1
    • 2
    • 3
  1. 1.Deptartment of Electrical and Computer EngineeringThe University of IowaIowa CityUSA
  2. 2.The Iowa Institute for Biomedical ImagingThe University of IowaIowa CityUSA
  3. 3.Deptartment of Internal MedicineThe University of IowaIowa CityUSA

Personalised recommendations