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论文:2013,Vol:31,Issue(2):218-222 |
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引用本文： |
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黄保山, 滕炯华, 徐婧林, 周三平. 基于偏移场修正的C-V模型水平集图像分割算法[J]. 西北工业大学 |
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Huang Baoshan, Teng Jionghua, Xu Jinglin, Zhou Sanping. A Nove Algorithm of Level Set Image Segmentation Based on Bias Field Correction C-V Model[J]. Northwestern polytechnical university |
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| 基于偏移场修正的C-V模型水平集图像分割算法 |
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| 黄保山, 滕炯华, 徐婧林, 周三平 |
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| 西北工业大学 自动化学院, 陕西 西安 710072 |
| 摘要: |
| 灰度不均匀效应广泛存在于现实图像(real-world images)中,这给图像分割带来了很大的挑战,目前许多的图像分割算法都依赖于图像灰度分布均匀这一假设,这严重影响了算法分割现实图像的分割精度。因此文章结合图像的数学模型,提出了一种基于偏差修正的C-V模型,该方法在水平集函数的演化过程中,同时进行图像的分割与偏移场的估计,利用偏移场的估计值来抑制灰度不均匀效应的影响。仿真结果表明,该方法比经典的C-V模型有更高的分割精度,对初始化轮廓曲线以及噪声有较强的鲁棒性。 |
| 关键词:
图像分割
偏移场修正
灰度不均匀效应
水平集
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| A Nove Algorithm of Level Set Image Segmentation Based on Bias Field Correction C-V Model |
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| Huang Baoshan, Teng Jionghua, Xu Jinglin, Zhou Sanping |
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| Department of Automatic Control,Northwestern Polytechnical University,Xi'an 710072,China |
| Abstract: |
| A real-world image often has intensity inhomogeneity, which severely challenges image segmentation.Currently, many image segmentation algorithms assume that the intensity distribution is homogeneous,and the as-sumption badly affects the precision of their real-world image segmentation. Therefore,using the C-V model,wepropose what we believe to be a novel algorithm of level set image segmentation,which eliminates the intensity in-homogeneity by correcting the bias field to suppress the intensity inhomogeneity. To verify the performance of ouralgorithm,we simulate the segmentation of both real-world images and artificial images. The simulation results,given in Figs. 1 through 3 show that our algorithm has better segmentation precision than that of the tradition C-Vmodel, can effective suppress the intensity inhomogeneity and noise interference and segment both the real-world im-age and the artifical image. |
| Key words:
image segmentation;bias field correction
Chan-Vese model
intensity inhomogeneity
level set
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| 收稿日期: 2012-05-10
修回日期:
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| DOI: |
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Email： |
| 作者简介: 黄保山(1986-),西北工业大学硕士研究生,主要从事数字图像处理的研究。
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参考文献: |
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[1] Tony F Chan,Luminita A Vese. Active Contours Without Edges. IEEE Trans on Image Processing, 2001, 10(2): 266-277
[2] Li Chunning,Kao Chiuyen,Gore John C. Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Transon Image Processing, 2008, 1940-1949
[3] Wang Li,Li Chunming,Sun Quansen. Active Contours Driven by Local and Global Intensity Fitting Energy with Application toBrain MR Image Segmentation. Computerized Imaging and Graphics, 2009, 520-531
[4] Li Chunming, Xu Chengyang, Gui Changfeng, et al. Level Set Formulation without Re-Initialization:a New Variational Formula-tion. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 1: 430-436 |
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相关文献: |
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1．周三平, 滕炯华, 黄保山, 徐婧林.基于边缘信息与偏移场矫正的多相Chan-Vese图像分割模型[J]. 西北工业大学, 2014,32(3): 434-439 |
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