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A Multiphase Entropy-Based Level Set Algorithm for MR Breast Image Segmentation Using Lattice Boltzmann Model

机译:基于格子Boltzmann模型的基于多相熵的MR图像分割水平集算法

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Recently, with the development of high dimensional large-scale medical imaging devices, the need of fast and accurate segmentation methods is increasing. In this paper, we propose a new variational multiphase level set approach to medical image segmentation. We first design an entropy-based energy functional, from which we derive the multiphase level set equations and a new entropic external forces for the lattice Boltzmann D2Q9 model. The method is accurate and highly parallelizable. The local nature of the LBM allows it to be suitable for fast segmentation methods implemented using some parallel devices such as the graphics processing unit. Experimental results on MR breast images demonstrate the effectiveness of the proposed method.
机译:近来,随着高尺寸大规模医学成像设备的发展,对快速准确的分割方法的需求正在增加。在本文中,我们提出了一种新的变分多相水平集方法来进行医学图像分割。我们首先设计一个基于熵的能量函数,从中导出多相能级方程组和晶格Boltzmann D2Q9模型的新的熵外力。该方法准确且高度可并行化。 LBM的本地性质使其适用于使用某些并行设备(例如图形处理单元)实现的快速分割方法。 MR乳房图像的实验结果证明了该方法的有效性。

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