论文标题
具有局部差异项项及其有效最小化求解器的主动轮廓模型用于多相图像分割
An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multi-phase Image Segmentation
论文作者
论文摘要
在本文中，我们提出了一个具有局部差异力（LVF）项的主动轮廓模型，该模型可以应用于多相图像分割问题。使用LVF，提出的模型在分割噪声的图像中非常有效。为了有效地解决该模型，我们通过特征函数表示正则化项，然后根据迭代卷积阈值方法（ICTM）（即ICTM-LVF）设计最小化算法。在某些条件下，这种最小化算法享有能源销售特性，并且在分割中具有高效的性能。为了克服主动轮廓模型的初始化问题，我们将不均匀的图形拉普拉斯初始化方法（Iglim）推广到多相案例，然后将其应用于ICTM-LVF求解器的初始轮廓。数值实验是在合成图像和真实图像上进行的，以证明我们初始化方法的能力，以及局部差异力在多相图像分割中噪声鲁棒性的有效性。
In this paper, we propose an active contour model with a local variance force (LVF) term that can be applied to multi-phase image segmentation problems. With the LVF, the proposed model is very effective in the segmentation of images with noise. To solve this model efficiently, we represent the regularization term by characteristic functions and then design a minimization algorithm based on a modification of the iterative convolution-thresholding method (ICTM), namely ICTM-LVF. This minimization algorithm enjoys the energy-decaying property under some conditions and has highly efficient performance in the segmentation. To overcome the initialization issue of active contour models, we generalize the inhomogeneous graph Laplacian initialization method (IGLIM) to the multi-phase case and then apply it to give the initial contour of the ICTM-LVF solver. Numerical experiments are conducted on synthetic images and real images to demonstrate the capability of our initialization method, and the effectiveness of the local variance force for noise robustness in the multi-phase image segmentation.
