A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method
A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method
复制标题
一种使用模糊聚类和格子玻尔兹曼方法进行图像分割的快速鲁棒水平集方法
DOI：
10.1109/tsmcb.2012.2218233
复制
发表时间：
2013-06-01
影响因子：
11.8
通讯作者：
Wang, Bin
中科院分区：
文献类型：
--
作者：
Balla-Arabe, Souleymane;Gao, Xinbo;Wang, Bin
在过去的几十年里，由于并行编程的发展，格子Boltzmann方法（LBM）作为求解偏微分方程的一种快速替代方法引起了人们的广泛关注。在本文中，我们首先设计了一个基于模糊c均值目标函数的能量泛函，其中包含了考虑真实世界图像强度不均匀性的偏置场。利用梯度下降法，我们得到了相应的水平集方程，我们推导出一个模糊外力的LBM求解器基于赵的模型。该方法是快速的，鲁棒的抗噪声，独立于初始轮廓的位置，有效的强度不均匀性的存在下，高度并行化，可以检测有或没有边缘的对象。在医学图像和真实图像上的实验证明了该方法在速度和效率方面的性能。
In the last decades, due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, we first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity inhomogeneity of the real-world image. Using the gradient descent method, we obtained the corresponding level set equation from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is fast, robust against noise, independent to the position of the initial contour, effective in the presence of intensity inhomogeneity, highly parallelizable and can detect objects with or without edges. Experiments on medical and real-world images demonstrate the performance of the proposed method in terms of speed and efficiency.