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Efficient and Robust Segmentations Based on Eikonal and Diffusion PDEs

机译:基于真核和扩散PDE的高效鲁棒分割

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摘要

In this paper, we present efficient and simple image segmentations based on the solution of two separate Eikonal equations, each originating from a different region. Distance functions from the interior and exterior regions are computed, and final segmentation labels are determined by a competition criterion between the distance functions. We also consider applying a diffusion partial differential equation (PDE) based method to propagate information in a manner inspired by the information propagation feature of the Eikonal equation. Experimental results are presented in a particular medical image segmentation application, and demonstrate the proposed methods.
机译:在本文中,我们基于两个独立的Eikonal方程的解给出了有效而简单的图像分割,每个方程均源自不同的区域。计算来自内部和外部区域的距离函数,并根据距离函数之间的竞争标准确定最终的分割标签。我们还考虑应用基于扩散偏微分方程(PDE)的方法,以受Eikonal方程信息传播特征启发的方式传播信息。实验结果在特定的医学图像分割应用中显示,并证明了所提出的方法。

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