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BrainPrint: Identifying Subjects by their Brain

Institution:
1Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA.
2Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
17
Issue Number:
Pt 3
Pages:
41-8
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):41-8.
PubMed ID:
25320780
PMCID:
PMC4216735
Appears in Collections:
NAC, NA-MIC, NCIGT
Sponsors:
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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Wachinger-MICCAI2014-3-fig2.jpg (89.846kB)