Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images
Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images
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发表时间：
1999
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通讯作者：
Chenyang Xu;Jerry L Prince
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作者：
Chenyang Xu;Jerry L Prince
从图像构建对象边界（边界映射）的数学表示是一个重要问题，对于图像分析、计算机视觉和医学成像等多个活跃的研究领域至关重要。本论文的重点是研究可变形模型，这是一种边界映射技术，它结合了图像信息和有关边界几何的先验知识，以提取有意义的边界描述。文献中报道的方法的一个关键问题是，当模型未在目标边界附近初始化或应用于重建具有凹面的边界时，它们难以可靠地映射边界。在这项研究中，我们对边界测绘领域做出了三个主要贡献。首先，我们开发了一种称为梯度矢量流变形模型的方法，该方法对模型初始化和边界凹性都具有鲁棒性。其次，我们开发了第一种方法的推广，可以提高收敛到狭窄边界压痕的性能，并提高定位边界的准确性。第三，我们开发了一种从磁共振图像重建人类大脑皮层中央层的方法，该方法使用我们提出的可变形模型作为核心组件。我们的方法在模拟图像和真实磁共振图像上得到了验证。
Constructing a mathematical representation of an object boundary (boundary mapping) from images is an important problem that is of importance to several active research areas such as image analysis, computer vision, and medical imaging. The focus of this dissertation is to investigate deformable models, a boundary mapping technique that incorporates both image information and prior knowledge about the boundary geometry to extract a meaningful boundary description. A key problem with methods reported in the literature is that they have difficulties in reliably mapping boundaries when the models are not initialized near target boundaries or are applied to reconstruct boundaries with concavities. In this research, we make three main contributions to the area of boundary mapping. First, we developed a method called the gradient vector flow deformable model that is robust to both model initialization and boundary concavities. Second, we developed a generalization of the first method that allows for improved performance in converging to narrow boundary indentations and greater accuracy in localizing boundaries. Third, we developed a method for reconstructing the central layer of the human cerebral cortex from magnetic resonance images that uses our proposed deformable model as a core component. Our methods are validated on both simulated images and real magnetic resonance images.