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Segmentation and Volume Representation Based on Spheres for Non-rigid Registration

机译:基于球体的非刚性配准分割与体表示

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This paper presents three different tasks: segmentation of medical images, volume representation and non-rigid registration. The first task is a necessary step before volume representation ant it is done with a simple but effective strategy using tomographic images, combining texture and boundary information in a region growing strategy, obtaining good results. For the second task, we present a new approach to model 2D surfaces and 3D volumetric data based on marching cubes idea using however spheres (modeling the surface of an object using spheres allows us to reduce the number of primitives representing it and to benefit -from such reduction- the registration process of two objects). We compare our approach based on marching cubes idea with other one using Delau-nay tetrahedrization, and the results show that our proposed approach reduces considerably the number of spheres. Finally, we show how to do non-rigid registration of two volumetric data represented as sets of spheres using 5-dimensional vectors in conformal geometric algebra.
机译:本文提出了三个不同的任务:医学图像分割,体积表示和非刚性配准。第一个任务是体积表示之前的必要步骤,这是使用简单但有效的使用断层图像的策略完成的,将纹理和边界信息组合到区域增长策略中,以获得良好的结果。对于第二个任务,我们提出了一种新方法,该方法基于使用球形的行进立方体思想对2D曲面和3D体积数据进行建模(使用球形建模对象的表面使我们能够减少表示该曲面的基元的数量并从中受益)这种减少-两个对象的注册过程)。我们将基于行进立方体思想的方法与使用Delau-nay四面体化的方法进行了比较,结果表明,我们提出的方法大大减少了球的数量。最后,我们展示了如何在保形几何代数中使用5维向量对表示为一组球体的两个体积数据进行非刚性配准。

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