首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >NON-RIGID REGISTRATION AND CORRESPONDENCE FINDING IN MEDICAL IMAGE ANALYSIS USING MULTIPLE-LAYER FLEXIBLE MESH TEMPLATE MATCHING
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NON-RIGID REGISTRATION AND CORRESPONDENCE FINDING IN MEDICAL IMAGE ANALYSIS USING MULTIPLE-LAYER FLEXIBLE MESH TEMPLATE MATCHING

机译:多层柔性网格模板匹配在医学图像分析中的非刚性配准和对应

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摘要

In this paper we present a novel technique for non-rigid medical image registration and correspondence finding based on a multiple-layer flexible mesh template matching technique. A statistical anatomical model is built in the form of a tetrahedral mesh, which incorporates both shape and density properties of the anatomical structure. After the affine transformation and global deformation of the model are computed by optimizing an energy function, a multiple-layer flexible mesh template matching is applied to find the vertex correspondence and achieve local deformation. The multiple-layer structure of the template can be used to describe different scale of anatomical features; furthermore, the template matching is flexible which makes the correspondence finding robust. A leave-one-out validation has been conducted to demonstrate the effectiveness and accuracy of our method.
机译:在本文中,我们提出了一种基于多层柔性网格模板匹配技术的非刚性医学图像配准和对应查找的新技术。以四面体网格的形式构建统计解剖模型,该模型结合了解剖结构的形状和密度特性。通过优化能量函数计算出模型的仿射变换和整体变形后,应用多层柔性网格模板匹配找到顶点对应关系并实现局部变形。模板的多层结构可用于描述不同尺度的解剖特征。此外,模板匹配是灵活的,这使得对应查找变得鲁棒。我们进行了留一法验证,以证明我们方法的有效性和准确性。

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