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BrainPrint: Identifying Subjects by their Brain

1Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA.
2Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
Issue Number:
Pt 3
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):41-8.
PubMed ID:
Appears in Collections:
P41 EB015896/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR014075/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Golland P., Reuter M. BrainPrint: Identifying Subjects by their Brain. Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):41-8. PMID: 25320780. PMCID: PMC4216735.
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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.

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