Statistical Atlases of Bone Anatomy: Construction, Iterative Improvement and Validation

  • Gouthami Chintalapani
  • Lotta M. Ellingsen
  • Ofri Sadowsky
  • Jerry L. Prince
  • Russell H. Taylor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)


We present an iterative bootstrapping framework to create and analyze statistical atlases of bony anatomy such as the human pelvis from a large collection of CT data sets. We create an initial tetrahedral mesh representation of the target anatomy and use deformable intensity-based registration to create an initial atlas. This atlas is used as prior information to assist in deformable registration/segmentation of our subject image data sets, and the process is iterated several times to remove any bias from the initial choice of template subject and to improve the stability and consistency of mean shape and variational modes. We also present a framework to validate the statistical models. Using this method, we have created a statistical atlas of full pelvis anatomy with 110 healthy patient CT scans. Our analysis shows that any given pelvis shape can be approximated up to an average accuracy of 1.5036 mm using the first 15 principal modes of variation. Although a particular intensity-based deformable registration algorithm was used to produce these results, we believe that the basic method may be adapted readily for use with any registration method with broadly similar characteristics.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gouthami Chintalapani
    • 1
  • Lotta M. Ellingsen
    • 1
  • Ofri Sadowsky
    • 1
  • Jerry L. Prince
    • 1
  • Russell H. Taylor
    • 1
  1. 1.Johns Hopkins University, BaltimoreUSA

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