This paper introduces a highly adaptive and automated approach for generating Finite Element (FE) discretization for a given realistic multi-compartment human head model obtained through magnetic resonance imaging (MRI) dataset. We aim at obtaining accurate tetrahedral FE meshes for electroencephalographic source localization. We present recursive solid angle labeling for the surface segmentation of the model and then adapt it with a set of smoothing, inflation, and optimization routines to further enhance the quality of the FE mesh. The results show that our methodology can produce FE mesh with an accuracy greater than 1 millimeter, significant with respect to both their 3D structure discretization outcome and electroencephalographic source localization estimates. FE meshes can be achieved for the human head including complex deep brain structures. Our algorithm has been implemented using the open Matlab-based Zeffiro Interface toolbox with it effective time-effective parallel computing system.}

翻译：本文提出了一种高度自适应与自动化方法，用于基于磁共振成像数据集构建的真实多隔室人脑模型生成有限元离散化网格。该方法旨在获得用于脑电图源定位的高精度四面体有限元网格。我们采用递归立体角标记法对模型表面进行分割，并配合平滑、膨胀及优化等系列处理流程进一步提升有限元网格质量。结果表明，该方法生成的有限元网格精度优于1毫米，在三维结构离散化结果与脑电图源定位估计两方面均具有显著优势。该网格可成功包含人脑复杂的深层脑结构。算法已基于开源Matlab平台Zeffiro Interface工具箱实现，并采用了高效并行计算系统。