Han Xiao, Pham Dzung L, Tosun Duygu, Rettmann Maryam E, Xu Chenyang, Prince Jerry L
Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 USA.
Neuroimage. 2004 Nov;23(3):997-1012. doi: 10.1016/j.neuroimage.2004.06.043.
Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.
从磁共振（MR）图像中分割并呈现人类大脑皮层在神经科学和医学中发挥着重要作用。一种成功的分割方法必须对各种成像伪影具有鲁棒性，并生成具有解剖学意义且一致的皮层表示。本文提出了一种从T1加权MR脑图像自动重建大脑皮层内表面、中心表面和外表面的方法。该方法结合了模糊组织分类方法、高效的拓扑校正算法和拓扑保持几何可变形表面模型（TGDM）。该算法速度快且数值稳定，能够生成准确的脑表面重建结果，保证拓扑正确且无自相交。文中给出了在真实MR数据上的验证结果，以证明该方法的性能。
