Wachinger Christian, Golland Polina, Reuter Martin
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):41-8. doi: 10.1007/978-3-319-10443-0_6.
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.
介绍BrainPrint，一种大脑中解剖结构的紧凑且具有区分性的表示。BrainPrint通过在三角形（边界）和四面体（体积）网格上求解二维和三维拉普拉斯 - 贝尔特拉米算子来捕获皮质和皮质下结构集合的形状信息。我们为这种表示推导了一个强大的分类器，该分类器基于脑部扫描数据库在新的扫描中识别受试者。在一个包含超过3000次MRI扫描的示例数据集中，我们表明BrainPrint捕获了有关受试者解剖结构的独特信息，并能够以超过99.8%的准确率正确分类扫描。获取紧凑表示的所有处理步骤都是完全自动化的，这使得这个处理框架对于处理大型数据集特别有吸引力。
