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A new tissue segmentation algorithm in 3d data based on boundary model and local character structure

机译:基于边界模型和本地字符结构的3D数据中的一种新组织分割算法

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Tissue segmentation in 3d data is an important technology in medical visualization, image segmentation and virtual endoscopy. It is difficulty to automatically and accurately implement tissue segmentation in 3d data because of its complexity. A semi-automatic tissue segmentation algorithm in 3d data is proposed based on boundary model and local character structure in this paper. We found out inner voexls and outer voexls by pre-appointed voxel based on boundary model. And then, boundary voexls are correctly classified into different tissues by their eigenvalues of Hessian matrix based on the local character structure. Only eigenvalues of the boundary voxels are computed, so little time is used compared with other algorithms based on local character structure. It can quickly and effectively realize the segmentation of single tissue.
机译:3D数据中的组织分割是医学可视化,图像分割和虚拟内窥镜检查中的重要技术。由于其复杂性,难以自动和准确地实现3D数据中的组织分割。基于本文的边界模型和局部字符结构提出了一种三维数据中的半自动组织分割算法。我们通过基于边界模型预先指定的体素发现内部Voexls和外部Voxl。然后,基于本地字符结构,通过Hessian矩阵的特征值正确分为不同组织的边界Voexl。仅计算边缘素的特征值,与基于本地字符结构的其他算法相比,使用了很少的时间。它可以快速有效地实现单个组织的分割。

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