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PARALLELIZATION FOR A NON-RIGID REGISTRATION USING ELASTIC MODEL, FINITE ELEMENT METHOD AND MUTUAL INFORMATION

机译:弹性模型,有限元方法和互信息的非刚性配准

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The aim is to parallelize an approach for non-rigid registration. We have led a survey on a new approach in non rigid registration using elastic model, finite element method and mutual information and we have had good results. Using a deformation algorithm, based on the isotropic linear elastic material model, one can get a good non-rigid registration result. This model allows to get image regularization characteristics. However, the Mattes et al. mutual information [1] is reputed to do a multimodal registration. Besides, one can use it for a registration with images from the same modality but in low resolutions, such as the Positron Emission Tomography (PET). This approach concerns a process with a fully automatic registration. However one can note that registration requires a too much important calculation time and high occupancy spaces especially with 3D images. Thus, the parallelization is required. So, this paper deals the transformation of the registration algorithm in a parallel environment. We have worked with Single Program Multiple Data model on Distributed Memory (SPMD-DM) architecture.
机译:目的是使非刚性注册的方法并行化。我们对使用弹性模型,有限元方法和互信息的非刚性配准的新方法进行了调查,并取得了良好的效果。使用变形算法,基于各向同性线性弹性材料模型,可以获得良好的非刚性配准结果。该模型可以获取图像正则化特征。但是,Mattes等人。相互信息[1]被称为进行多模式注册。此外,可以使用它注册具有相同模态但分辨率较低的图像,例如正电子发射断层扫描(PET)。该方法涉及具有全自动注册的过程。但是,您可能会注意到,配准需要过多的重要计算时间和高占用空间,尤其是3D图像。因此,需要并行化。因此,本文探讨了并行环境中注册算法的转换。我们已经在分布式内存(SPMD-DM)体系结构上使用了单程序多数据模型。

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