Radiological Physics and Technology

, Volume 7, Issue 2, pp 277–283 | Cite as

Development and evaluation of statistical shape modeling for principal inner organs on torso CT images

  • Xiangrong ZhouEmail author
  • Rui Xu
  • Takeshi Hara
  • Yasushi Hirano
  • Ryujiro Yokoyama
  • Masayuki Kanematsu
  • Hiroaki Hoshi
  • Shoji Kido
  • Hiroshi Fujita


The shapes of the inner organs are important information for medical image analysis. Statistical shape modeling provides a way of quantifying and measuring shape variations of the inner organs in different patients. In this study, we developed a universal scheme that can be used for building the statistical shape models for different inner organs efficiently. This scheme combines the traditional point distribution modeling with a group-wise optimization method based on a measure called minimum description length to provide a practical means for 3D organ shape modeling. In experiments, the proposed scheme was applied to the building of five statistical shape models for hearts, livers, spleens, and right and left kidneys by use of 50 cases of 3D torso CT images. The performance of these models was evaluated by three measures: model compactness, model generalization, and model specificity. The experimental results showed that the constructed shape models have good “compactness” and satisfied the “generalization” performance for different organ shape representations; however, the “specificity” of these models should be improved in the future.


CT images 3D organ shapes Statistical shape model Point distribution model Minimum description length 



The authors thank members of the Fujita Laboratory. This research work was funded in part by a Grant-in-Aid for Scientific Research on Innovative Areas, and in part by a Grant-in-Aid for Scientific Research, MEXT, Japan.

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Frumin M, Golland P, Kikinis R, Hirayasu Y, Salisbury DF, Hennen J, Dickey CC, Anderson M, Jolesz FA, Grimson WE, McCarley RW, Shenton ME. Shape differences in the corpus callosum in first-episode schizophrenia and first-episode psychotic affective disorder. Am J Psychiatry. 2002;159:866–8.PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Nappi J, Frimmel H, Yoshida H. Virtual endoscopic visualization of the colon by shape-scale signatures. Inf Technol Biomed IEEE Trans. 2005;9:120–31.CrossRefGoogle Scholar
  3. 3.
    Yoshida H, Nappi J, MacEneaney P, Rubin DT, Dachman AH. Computer-aided diagnosis scheme for detection of polyps at CT colonography. Radiographics. 2002;22:963–79.PubMedCrossRefGoogle Scholar
  4. 4.
    Berks M, Caulkin S, Rahim R, Boggis C, Astley S. Statistical appearance models of mammographic masses. Proc IWDM. 2008;2008:401–8.Google Scholar
  5. 5.
    Heimann T, Meinzer H. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13:543–63.PubMedCrossRefGoogle Scholar
  6. 6.
    Cootes T, Taylor C, Cooper D, Graham J. Active shape models—their training and application. Comput Vision Image Underst. 1995;61:38–59.CrossRefGoogle Scholar
  7. 7.
    Cootes T, Hill A, Taylor C, Haslam J. The use of active shape models for locating structures in medical images. Image Vis Comput. 1994;12:355–66.CrossRefGoogle Scholar
  8. 8.
    Yamaguchi S, Zhou X, Xu R, Hara T, Yokoyama R, Kanematsu M, Hoshi H, Kido S, Fujita H. Construction of statistical shape models of organs in torso CT scans using MDL method. Proc Int Forum Med Imaging Asia. 2012;2012:2–33.Google Scholar
  9. 9.
    Xu R, Zhou X, Hirano Y, Tachibana R, Hara T, Kido S, Fujita H. Evaluation of group-wise based methods for statistical shape models construction. Japanese Society of Medical Imaging Technology 2011. 2011; CD-ROM, OP1-7.Google Scholar
  10. 10.
    Yamagichi S, Hayashi T, Zhou X, Hara T, Yokoyama R, Kanematsu M, Hoshi H, Fujita H. An interactive method for organ region segmentation in X-ray CT images, Japanese Society of Medical Imaging Technology 2011. 2011; CD-ROM, OP1-8.Google Scholar
  11. 11.
    William EL, Harvey EC. Marching Cubes: a high resolution 3D surface construction algorithm. Comput Graph. 1987;21:163–9.CrossRefGoogle Scholar
  12. 12.
    The Visualization Toolkit (VTK). Accessed 25 Feb 2014.
  13. 13.
    Davies R, Twining C, Cootes T. A minimum description length approach to statistical shape modeling. IEEE Trans Med Imaging. 2002;21:525–37.PubMedCrossRefGoogle Scholar
  14. 14.
    Heimann T, Wolf I, Williams T, Meinzer H. 3D active shape models using gradient descent optimization of description length. Proc IPMI’05. 2005; 3565:566–77.Google Scholar
  15. 15.
    Xu R, Zhou X, Hirano Y, Tachibana R, Hara T, Kido S, Fujita H. Particle-system based adaptive sampling on spherical parameter space to improve the MDL method for construction of statistical shape models. Comput Math Methods Med. 2013;2013:1–9, Article ID 196259.Google Scholar
  16. 16.
    Pizer SM, Fletcher PT, et al. Deformable m-reps for 3D medical image segmentation. Int J Comput Vision. 2003;55:85–106.CrossRefGoogle Scholar
  17. 17.
    Székely G, Kelemen A, Brechbühler C, Gerig G. Segmentation of 2-D and 3-D objects form MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models. Med Image Anal. 1996;1:19–34.PubMedCrossRefGoogle Scholar

Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2014

Authors and Affiliations

  • Xiangrong Zhou
    • 1
    Email author
  • Rui Xu
    • 2
  • Takeshi Hara
    • 1
  • Yasushi Hirano
    • 2
  • Ryujiro Yokoyama
    • 1
  • Masayuki Kanematsu
    • 3
  • Hiroaki Hoshi
    • 4
  • Shoji Kido
    • 2
  • Hiroshi Fujita
    • 1
  1. 1.Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of MedicineGifu UniversityGifuJapan
  2. 2.Graduate School of MedicineYamaguchi UniversityUbeJapan
  3. 3.Department of RadiologyGifu University HospitalGifuJapan
  4. 4.Department of Radiology, Division of Tumor Control, Graduate School of MedicineGifu UniversityGifuJapan

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