A local region-based Chan-Vese model for image segmentation
A local region-based Chan-Vese model for image segmentation
复制标题
基于局部区域的图像分割 Chan-Vese 模型
DOI：
10.1016/j.patcog.2011.11.019
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发表时间：
2012-07-01
影响因子：
8
通讯作者：
Peng, Yali
中科院分区：
文献类型：
--
作者：
Liu, Shigang;Peng, Yali
本文提出了一种新的基于区域的活动轮廓模型，即基于局部区域的Chan-Vese （LRCV）模型。通过考虑图像局部特征，该模型可以有效地分割具有强度非均匀性的图像。为了减少许多活动轮廓模型对人工初始化的依赖，实现自动分割，提出了一种退化的CV模型，其分割结果可作为LRCV模型的初始轮廓。此外，我们利用高斯滤波对水平集函数进行正则化，使其在进化过程中保持平滑。在合成图像和真实图像上的实验结果表明了该方法的有效性和鲁棒性。与已知的局部二值拟合（LBF）模型相比，该方法的计算效率大大提高，而且对初始轮廓的敏感性大大降低。(C) 2012 Elsevier Ltd.版权所有。
In this paper, a new region-based active contour model, namely local region-based Chan-Vese (LRCV) model, is proposed for image segmentation. By considering the image local characteristics, the proposed model can effectively and efficiently segment images with intensity inhomogeneity. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a degraded CV model is proposed, whose segmentation result can be taken as the initial contour of the LRCV model. In addition, we regularize the level set function by using Gaussian filtering to keep it smooth in the evolution process. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the well-know local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour. (C) 2012 Elsevier Ltd. All rights reserved.