title: BrainPrint: identifying subjects by their brain.
abstract: 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.
authors.forename: Christian
authors.initials: C
authors.lastname: Wachinger
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
authors.forename: Martin
authors.initials: M
authors.lastname: Reuter
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D056667:Biometric Identification / Q000379:methods*; D001921:Brain / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013382:Subtraction Technique