2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

结合FCMS与变分水平集的图像分割模型

唐利明 田学全 黄大荣 王晓峰

唐利明, 田学全, 黄大荣, 王晓峰. 结合FCMS与变分水平集的图像分割模型. 自动化学报, 2014, 40(6): 1233-1248. doi: 10.3724/SP.J.1004.2014.01233
引用本文: 唐利明, 田学全, 黄大荣, 王晓峰. 结合FCMS与变分水平集的图像分割模型. 自动化学报, 2014, 40(6): 1233-1248. doi: 10.3724/SP.J.1004.2014.01233
TANG Li-Ming, TIAN Xue-Quan, HUANG Da-Rong, WANG Xiao-Feng. Image Segmentation Model Combined with FCMS and Variational Level Set. ACTA AUTOMATICA SINICA, 2014, 40(6): 1233-1248. doi: 10.3724/SP.J.1004.2014.01233
Citation: TANG Li-Ming, TIAN Xue-Quan, HUANG Da-Rong, WANG Xiao-Feng. Image Segmentation Model Combined with FCMS and Variational Level Set. ACTA AUTOMATICA SINICA, 2014, 40(6): 1233-1248. doi: 10.3724/SP.J.1004.2014.01233

结合FCMS与变分水平集的图像分割模型

doi: 10.3724/SP.J.1004.2014.01233
基金项目: 

国家自然科学基金(61004118)资助

详细信息
    作者简介:

    田学全 重庆科技学院数理学院副教授.主要研究方向为粗糙集,数据挖掘,运筹与优化. E-mail:tianxueq@126.com

Image Segmentation Model Combined with FCMS and Variational Level Set

Funds: 

Supported by National Natural Science Foundation of China (61004118)

  • 摘要: 提出了一个结合融合空间约束的模糊C均值(Fuzzy C means with spatial constraints,FCMS)聚类与变分水平集的图像模糊聚类分割模型.在该模型中引入了一个基于图像局部信息和空间信息的外部模糊聚类能量,从而可以获取精确的局部图像的空间特征,使得本文模型对噪声图像的聚类分割具有较强的鲁棒性.采用不同类型的实验图像,将本文模型与10个不同类型的图像分割模型进行了对比实验,实验结果显示本文模型能克服图像中噪声影响并取得较满意的聚类分割结果.
  • [1] Chan T F, Vese L A. Active contours without edges. IEEE Transactions on Image Processing, 2001, 10(2): 266-277
    [2] Zheng Qiang, Dong En-Qing. Narrow band active contour model for local segmentation of medical and texture images. Acta Automatica Sinica, 2013, 39(1): 21-30(郑强, 董恩清. 窄带主动轮廓模型及在医学和纹理图像局部分割中的应用. 自动化学报, 2013, 39(1): 21-30)
    [3] Li C M, Huang R, Ding Z H, Gatenby J C, Metaxas D N, Gore J C. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on Image Processing, 2011, 20(7): 2007-2016
    [4] Liu Bo, Huang Jian-Hua, Tang Xiang-Long, Liu Jia-Feng, Zhang Ying-Tao. Combining global probability density difference and local gray level fitting for ultrasound image segmentation. Acta Automatica Sinica, 2010, 36(7): 951-959(刘博, 黄剑华, 唐降龙, 刘家锋, 张英涛. 窄结合全局概率密度差异与局部灰度拟合的超声图像分割. 自动化学报, 2010, 36(7): 951-959)
    [5] Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 1989, 42(5): 577-685
    [6] Vese L A, Chan T F. A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 2002, 50(3): 271-293
    [7] Jung Y M, Kang S H, Shen J H. Multiphase image segmentation via Modica-Mortola phase transition. SIAM Journal of Applied Mathematics, 2007, 67(5): 1213-1232
    [8] Lie J, Lysaker M, Tai X C. A binary level set model and some applications to Mumford-Shah image segmentation. IEEE Transactions on Image Processing, 2006, 15(5): 1171 -1181
    [9] Gao S B, Yan Y Y. Brain MR image segmentation via a multiphase level set approach. Journal of Information and Computational Science, 2012, 16(9): 4705-4711
    [10] Dunn J C. A graph theoretic analysis of pattern classification via Tamura's fuzzy relation. IEEE Transactions on Systems, Man, and Cybernetics, 1974, SMC-4(3): 310-313
    [11] Bezdek J C. A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, PAMI-2(1): 1-8
    [12] Cao H B, Deng H W, Wang Y P. Segmentation of M-FISH images for improved classification of chromosomes with an adaptive fuzzy C-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 2012, 20(1): 1-8
    [13] Ahmed M N, Yamany S M, Mohamed N, Farag A A, Moriarty T. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199
    [14] Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(4): 1907-1916
    [15] Yang, M S, Tsai H S. A Gaussian kernel-based fuzzy C-means algorithm with a spatial bias correction. Pattern Recognition Letters, 2008, 29(12): 1713-1725
    [16] Kannan S R, Ramathilagam S, Sathya A, Pandiyarajan R. Effective fuzzy C-means based kernel function in segmenting medical images. Computers in Biology and Medicine, 2010, 40(6): 572-579
    [17] Liu H Q, Zhao F, Jiao L C. Fuzzy spectral clustering with robust spatial information for image segmentation. Applied Soft Computing, 2012, 12(11): 3636-3647
    [18] He Y Y, Hussaini M Y, Ma J W, Shafei B, Steidl G. A new fuzzy C-means method with total variation regularization for segmentation of images with noisy and incomplete data. Pattern Recognition, 2012, 45(9): 3463-3471
    [19] Samson C, Blanc-Féraud L, Aubert G, Zerubia J. A level set model for image classification. International Journal of Computer Vision, 2000, 40(3): 187-197
    [20] Ray N, Acton S T. Image segmentation by curve evolution with clustering. In: Proceedings of the 2000 Conference Record of the 24th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA: IEEE, 2000. 495-458
    [21] Gibou F, Fedkiw R. A fast hybrid k-means level set algorithm for segmentation. In: Proceedings of the 4th Annual Hawaii Intentional Conference on Statistics and Mathematics. Honolulu, Hawaii, USA: American Statistical Association, 2005. 281-291
    [22] Xie Zhen-Ping, Wang Shi-Tong. An extended Mumford-Shah model integrated with fuzzy clustering. Acta Electronica Sinica, 2008, 36(1): 127-132(谢振平, 王士同. 融合模糊聚类的Mumford-Shah模型. 电子学报, 2008, 36(1): 127-132)
    [23] Li B N, Chui C K, Chang S, Ong S H. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine, 2011, 41(1): 1-10
    [24] Li C M, Xu C Y, Gui C F, Fox M D. Level set evolution without re-initialization: a new variational formulation. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 430-436
  • 加载中
计量
  • 文章访问数:  1599
  • HTML全文浏览量:  60
  • PDF下载量:  656
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-12-11
  • 修回日期:  2013-07-16
  • 刊出日期:  2014-06-20

目录

    /

    返回文章
    返回