A Bayesian model for joint segmentation and registration
A Bayesian model for joint segmentation and registration
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DOI：
10.1016/j.neuroimage.2005.11.044
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发表时间：
2006-05-15
期刊：
影响因子：
5.7
通讯作者：
Wells, William M.
中科院分区：
文献类型：
--
作者：
Pohl, Kilian A.;Fisher, John;Wells, William M.
提出了一种统计模型，该模型将图谱的配准与磁共振图像的分割相结合。我们使用基于期望最大化的算法在模型中找到一个解决方案，该模型同时估计图像伪影、解剖标签图和从图谱到图像空间的结构相关分层映射。该算法产生脑组织以及它们的子结构的分割。我们展示了一组22个磁共振图像的方法。在这组图像上，新方法的性能明显优于顺序应用配准和分割的类似方法。(c)2005年爱思唯尔公司All rights reserved.
A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to rind a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation. (c) 2005 Elsevier Inc. All rights reserved.