Persistence Diagrams of Cortical Surface Data

  • Moo K. Chung
  • Peter Bubenik
  • Peter T. Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5636)


We present a novel framework for characterizing signals in images using techniques from computational algebraic topology. This technique is general enough for dealing with noisy multivariate data including geometric noise. The main tool is persistent homology which can be encoded in persistence diagrams. These diagrams visually show how the number of connected components of the sublevel sets of the signal changes. The use of local critical values of a function differs from the usual statistical parametric mapping framework, which mainly uses the mean signal in quantifying imaging data. Our proposed method uses all the local critical values in characterizing the signal and by doing so offers a completely new data reduction and analysis framework for quantifying the signal. As an illustration, we apply this method to a 1D simulated signal and 2D cortical thickness data. In case of the latter, extra homological structures are evident in an control group over the autistic group.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Moo K. Chung
    • 1
    • 2
  • Peter Bubenik
    • 3
  • Peter T. Kim
    • 4
  1. 1.Department of Biostatistics and Medical InformaticsUSA
  2. 2.Waisman Laboratory for Brain Imaging and BehaviorUniversity of WisconsinMadisonUSA
  3. 3.Department of MathematicsCleveland State UniversityClevelandUSA
  4. 4.Department of Mathematics and StatisticsUniversity of GuelphGuelphCanada

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