Automated Segmentation of Cerebral Aneurysms Based on Conditional Random Field and Gentle Adaboost

  • Hong Zhang
  • Yuanfeng Jiao
  • Yongjie Zhang
  • Kenji Shimada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7599)


Quantified geometric characteristics of cerebral aneurysms such as volume, height, maximum diameter, surface area and aspect ratio are useful for predicting the rupture risk. Moreover, a newly developed fluid structure interaction system requires healthy models generated from the aneurysms to calculate anisotropic material directions for more accurate wall stress estimation. Thus the isolation of aneurysms is a critical step which currently depends primarily on manual segmentation. We propose an automated solution to this problem based on conditional random field and gentle adaboost. The proposed method was validated with eight datasets and four-fold cross-validation, an accuracy of 89.63%±3.09% is obtained.


Cerebral Aneurysm Segmentation Isolation Conditional Random Field Gentle Adaboost 


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  1. 1.
    Brisman, J.L., Song, J.K., Newell, D.W.: Cerebral aneurysms. New England Journal of Medicine 355(9), 928–939 (2006)CrossRefGoogle Scholar
  2. 2.
    NINDS. Cerebral aneurysm fact sheet. NIH Publication (08-5505) (2008)Google Scholar
  3. 3.
    Ma, B., Harbaugh, R.E., Raghavan, M.L.: Three-dimensional geometrical characterization of cerebral aneurysms. Annals of Biomedical Engineering 32(2), 264–273 (2004)CrossRefGoogle Scholar
  4. 4.
    Zhang, H., Jiao, Y., Johnson, E., Zhang, Y., Shimada, K.: Modeling anisotropic material property of cerebral aneurysms for fluid-structure interaction computational simulation. In: Third Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications, COMPIMAGE 2012, p. 6 (2012)Google Scholar
  5. 5.
    McLaughlin, R.A., Noble, J.A.: Demarcation of Aneurysms Using the Seed and Cull Algorithm. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part I. LNCS, vol. 2488, pp. 419–426. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Sgouritsa, E., Mohamed, A., Morsi, H., Shaltoni, H., Mawad, M.E., Kakadiaris, I.A.: Neck localization and geometry quantification of intracranial aneurysms. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1057–1060 (2010)Google Scholar
  7. 7.
    Baloch, S., Cheng, E., Zhu, Y., Mohamed, A., Ling, H., Fang, T.: Shape based conditional random fields for segmenting intracranial aneurysms. In: Workshop on Mesh Processing in Medical Image Analysis in Conjunction with MICCAI, 12 pages (2011)Google Scholar
  8. 8.
    Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3D mesh segmentation and labeling. ACM Transactions on Graphics (TOG) 29(3), 102 (2010)Google Scholar
  9. 9.
    Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)Google Scholar
  10. 10.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  11. 11.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 28(2), 337–407 (2000)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Garimella, R.V., Swartz, B.K.: Curvature estimation for unstructured triangulations of surfaces. Tech. Rep. LA-UR-03-8240, Los Alamos National Laboratory (2003)Google Scholar
  13. 13.
    Shapira, L., Shamir, A., Cohen-Or, D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. The Visual Computer 24(4), 249–259 (2008)CrossRefGoogle Scholar
  14. 14.
    Liu, R., Zhang, H., Shamir, A., Cohen-Or, D.: A part-aware surface metric for shape analysis. Computer Graphics Forum 28(2), 397–406 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, E., Mischaikow, K., Turk, G.: Feature-based surface parameterization and texture mapping. ACM Transactions on Graphics (TOG) 24(1), 1–27 (2005)CrossRefGoogle Scholar
  16. 16.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)CrossRefGoogle Scholar
  17. 17.
    Körtgen, M., Park, G.J., Novotni, M., Klein, R.: 3D shape matching with 3D shape contexts. In: The 7th Central European Seminar on Computer Graphics, vol. 3, pp. 5–17 (2003)Google Scholar
  18. 18.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 433–449 (1999)CrossRefGoogle Scholar
  19. 19.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics 21(3), 660–674 (1991)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hong Zhang
    • 1
  • Yuanfeng Jiao
    • 2
  • Yongjie Zhang
    • 1
    • 2
  • Kenji Shimada
    • 1
    • 2
  1. 1.Department of Mechanical EngineeringCarnegie Mellon UniversityUSA
  2. 2.Department of Biomedical EngineeringCarnegie Mellon UniversityUSA

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