BrainPrint : Identifying Subjects by Their Brain

  • Christian Wachinger
  • Polina Golland
  • Martin Reuter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)


Introducing BrainPrint, a compact and discriminative representation of anatomical structures in the brain. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. We derive a robust classifier for this representation that identifies the subject in a new scan, based on a database of brain scans. In an example dataset containing over 3000 MRI scans, we show that BrainPrint captures unique information about the subject’s anatomy and permits to correctly classify a scan with an accuracy of over 99.8%. All processing steps for obtaining the compact representation are fully automated making this processing framework particularly attractive for handling large datasets.


Shape Descriptor Subcortical Structure Tetrahedral Mesh Cortical Structure Subject Identity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christian Wachinger
    • 1
    • 2
  • Polina Golland
    • 1
  • Martin Reuter
    • 1
    • 2
  1. 1.Computer Science and Artificial Intelligence LabMITCambridgeUS
  2. 2.Harvard Medical SchoolMassachusetts General HospitalBostonUS

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