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A Restarted Iterative Homotopy Analysis Method for Three-dimensional Image Segmentation

机译:三维图像分割重启迭代同型分析方法

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Total variational segmentation models provide effective tools for identifying all features and their boundaries in two and three dimensional images and have been proven to be useful and successful. Speeding up a simulation is one of the remaining challenges. In this paper we propose a restarted homotopy analysis method to improve the computational efficiency in three-dimensional image segmentation. The algorithm replaces the nonlinear variational problem by a sequence of linear approximations by working with linear equations instead of nonlinear ones which lead to efficient energy minimization while maintaining the segmentation quality. Experimental results will show that the computational efficiency is significantly improved.
机译:总变性分割模型提供了有效的工具,用于识别两个和三维图像中的所有特征及其界限,并且已被证明是有用和成功的。 加速模拟是剩下的挑战之一。 在本文中,我们提出了重启的同型同型分析方法,以提高三维图像分割中的计算效率。 该算法通过使用线性方程而不是非线性方程来替换一系列线性近似的非线性变分问题,而不是非线性方程,这导致了在保持分割质量的同时有效的能量最小化。 实验结果将显示计算效率显着提高。

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