Segmentation of brain 3D MR images using level sets and dense registration
Segmentation of brain 3D MR images using level sets and dense registration
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DOI：
10.1016/s1361-8415(01)00039-1
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发表时间：
2001-09-01
影响因子：
10.9
通讯作者：
Barillot, C
中科院分区：
文献类型：
--
作者：
Baillard, C;Hellier, P;Barillot, C
本文提出了一种结合3D分割和3D配准过程的体磁共振图像脑分割策略。分割过程基于水平集的形式。闭合的3D曲面通过4D隐函数的迭代演化向所需边界传播。在这项工作中，传播依赖于一个包含自适应参数的稳健演化模型。这取决于输入数据和统计分布模型。本文的主要贡献是使用自动配准方法对曲面进行初始化，作为人工初始化的替代方案，通过稳健的多分辨率和多重网格最小化方案来实现配准。这种耦合显著提高了方法的质量，因为分割更快、更可靠和完全自动化。对合成的和真实的脑体积磁共振图像的定量和定性结果进行了介绍和讨论。(C)2001 Elsevier Science B.V.保留所有权利。
This paper presents a strategy for the segmentation of brain from volumetric MR images which integrates 3D segmentation and 3D registration processes. The segmentation process is based on the level set formalism. A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. In this work, the propagation relies on a robust evolution model including adaptive parameters. These depend on the input data and on statistical distribution models. The main contribution of this paper is the use of an automatic registration method to initialize the surface, as an alternative solution to manual initialization, The registration is achieved through a robust multiresolution and multigrid minimization scheme. This coupling significantly improves the quality of the method, since the segmentation is faster, more reliable and fully automatic. Quantitative and qualitative results on both synthetic and real volumetric brain MR images are presented and discussed. (C) 2001 Elsevier Science B.V. All rights reserved.