Split Bregman method for minimization of improved active contour model combining local and global information dynamically
Split Bregman method for minimization of improved active contour model combining local and global information dynamically
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DOI：
10.1016/j.jmaa.2011.11.073
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发表时间：
2012-05-01
影响因子：
1.3
通讯作者：
Wu, Boying
中科院分区：
文献类型：
--
作者：
Yang, Yunyun;Wu, Boying
结合Chan-Vese模型、区域可伸缩拟合能量模型、全局凸分割方法和分裂Bregman方法，提出了一种改进的活动轮廓模型。该算法采用一个随图像位置变化的权函数，动态地控制局部和全局信息的影响。首先，我们提出了我们的模型在2阶段的水平集制定，然后将其扩展到一个多阶段制定。该模型综合考虑了图像的局部和全局信息，能够分割出更一般的图像，尤其是灰度不均匀的图像。我们的模型已被应用到合成和真实的图像与有前途的结果。数值结果表明了该模型与其他模型相比的优越性。数值结果表明了该方法的有效性和准确性。此外，我们的模型在噪声存在的情况下是鲁棒的。(C)2011 Elsevier Inc. All rights reserved.
This paper presents an improved active contour model by combining the Chan-Vese model, the region-scalable fitting energy model, the globally convex segmentation method and the split Bregman method. A weight function that varies with the location of a given image is used to control the influence of the local and global information dynamically. We first present our model in a 2-phase level set formulation and then extend it to a multi-phase formulation. By taking the local and global information into consideration together, our model can segment more general images, especially images with intensity inhomogeneity. Our model has been applied to synthetic and real images with promising results. Numerical results show the advantages of our model compared with other models. The accuracy and efficiency are demonstrated by the numerical results. Besides, our model is robust in the presence of noise. (C) 2011 Elsevier Inc. All rights reserved.