Metric Space Structures for Computational Anatomy

  • Jianqiao Feng
  • Xiaoying Tang
  • Minh Tang
  • Carey Priebe
  • Michael Miller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)


This paper describes a method based on metric structures for anatomical analysis on a large set of brain MR images. A geodesic distance between each pair was measured using large deformation diffeomorphic metric mapping (LDDMM). Manifold learning approaches were applied to seek a low-dimensional embedding in the high- dimensional shape space, in which inference between healthy control and disease groups can be done using standard classification algorithms. In particular, the proposed method was evaluated on ADNI, a dataset for Alzheimer’s disease study. Our work demonstrates that the high-dimensional anatomical shape space of the amygdala and hippocampi can be approximated by a relatively low dimension manifold.


structural MR image computational anatomy Alzheimer’s disease manifold learning shape analysis 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Jianqiao Feng
    • 1
    • 2
  • Xiaoying Tang
    • 1
    • 2
  • Minh Tang
    • 1
    • 3
  • Carey Priebe
    • 1
    • 3
  • Michael Miller
    • 1
    • 4
  1. 1.Center of Imaging ScienceThe Johns Hopkins UniversityUnited States
  2. 2.Department of Electrical and Computer EngineeringThe Johns Hopkins UniversityUnited States
  3. 3.Department of Applied Mathematics and StatisticsThe Johns Hopkins UniversityUnited States
  4. 4.Department of Biomedical EngineeringThe Johns Hopkins UniversityUnited States

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