A hybrid framework for 3D medical image segmentation.
A hybrid framework for 3D medical image segmentation.
复制标题
3D 医学图像分割的混合框架。
DOI：
10.1016/j.media.2005.04.004
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发表时间：
2005
影响因子：
10.9
通讯作者：
Metaxas,Dimitris
中科院分区：
文献类型：
--
作者：
Chen,Ting;Metaxas,Dimitris
在本文中，我们提出了一种新的混合三维分割框架，结合吉布斯模型，行军立方体和变形模型。在此框架下，我们首先构造了一个新的吉布斯模型，其能量函数定义在一个高阶团系统上。新模型在分割过程中包含了区域和边界信息。其次，我们改进了原有的移动立方体方法，从吉布斯模型的输出来构造三维网格。3D网格用作可变形模型的初始几何形状。然后，我们变形的可变形模型使用外部图像力，使模型收敛到物体表面。我们通过使用可变形模型分割结果中的区域和边界信息更新Gibbs模型的参数来递归地运行Gibbs模型和可变形模型。在我们的方法中，基于区域的方法和基于边界的方法的混合组合的结果在复杂结构的改进的分割。该方法的好处是，它使用很少的先验信息和最少的用户干预产生高质量的3D结构分割。这种细分方法中的模块是在Insight ToolKit（ITK）的背景下开发的。我们目前的实验分割结果的脑肿瘤和评估我们的方法通过比较实验结果与专家手动分割。评价结果表明，该方法实现了高质量的分割结果与计算效率。我们还提出了其他临床对象的分割结果，以说明作为一个通用的分割框架的方法的强度。
In this paper we propose a novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. In the framework, first we construct a new Gibbs model whose energy function is defined on a high order clique system. The new model includes both region and boundary information during segmentation. Next we improve the original marching cubes method to construct 3D meshes from Gibbs models’ output. The 3D mesh serves as the initial geometry of the deformable model. Then we deform the deformable model using external image forces so that the model converges to the object surface. We run the Gibbs model and the deformable model recursively by updating the Gibbs model’s parameters using the region and boundary information in the deformable model segmentation result. In our approach, the hybrid combination of region-based methods and boundary-based methods results in improved segmentations of complex structures. The benefit of the methodology is that it produces high quality segmentations of 3D structures using little prior information and minimal user intervention. The modules in this segmentation methodology are developed within the context of the Insight ToolKit (ITK). We present experimental segmentation results of brain tumors and evaluate our method by comparing experimental results with expert manual segmentations. The evaluation results show that the methodology achieves high quality segmentation results with computational efficiency. We also present segmentation results of other clinical objects to illustrate the strength of the methodology as a generic segmentation framework.