Active contours driven by local image fitting energy

被引:589
|
作者
Zhang, Kaihua [1 ]
Song, Huihui [2 ]
Zhang, Lei [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
[2] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China
关键词
Image segmentation; Chan-Vese (C-V) model; Active contour models; LBF model; MUMFORD; SEGMENTATION;
D O I
10.1016/j.patcog.2009.10.010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A new region-based active contour model that embeds the image local information is proposed in this paper. By introducing the local image fitting (LIF) energy to extract the local image information, our model is able to segment images with intensity inhomogeneities. Moreover, a novel method based on Gaussian filtering for variational level set is proposed to regularize the level set function. It can not only ensure the smoothness of the level set function, but also eliminate the requirement of re-initialization, which is very computationally expensive. Experiments show that the proposed method achieves similar results to the LBF (local binary fitting) energy model but it is much more computationally efficient. In addition, our approach maintains the sub-pixel accuracy and boundary regularization properties. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1199 / 1206
页数:8
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