Non-local spatial spectral clustering for image segmentation

被引:24
作者
Liu, H. Q. [1 ]
Jiao, L. C. [1 ]
Zhao, F. [1 ]
机构
[1] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Inst Intelligent Informat Proc, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
Image segmentation; Spectral clustering; Weighted kernel k means; Non local spatial information; Magnetic resonance (MR) image; KERNEL; CUTS;
D O I
10.1016/j.neucom.2010.08.021
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As one of widely used clustering algorithms spectral clustering clusters data using the eigenvectors of the Laplacian matrix derived from a dataset and has been successfully applied to Image segmentation However spectral clustering algorithms are sensitive to noise and other imaging artifacts because of not taking into account the spatial information of the pixels in the image In this paper a novel non-local spatial spectral clustering algorithm for image segmentation is presented In the proposed method the objective function of weighted kernel k-means algorithm is firstly modified by incorporating the non-local spatial constraint term Then the equivalence between the objective functions of normalized cut and weighted kernel k-means with non-local spatial constraints is given and a novel non-local spatial matrix is constructed to replace the normalized Laplacian matrix Finally spectral clustering techniques are applied to this matrix to obtain the final segmentation result The novel algorithm is performed on synthetic and real images especially magnetic resonance (MR) images and compared with the traditional spectral clustering algorithms and segmentation algorithms with spatial information Experimental results demonstrate that the proposed algorithm is robust to noise in the image and obtains more effective performance than the comparison algorithms (C) 2010 Elsevier B V All rights reserved
引用
收藏
页码:461 / 471
页数:11
相关论文
共 26 条
  • [1] A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data
    Ahmed, MN
    Yamany, SM
    Mohamed, N
    Farag, AA
    Moriarty, T
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2002, 21 (03) : 193 - 199
  • [2] Learning eigenfunctions links spectral embedding and kernel PCA
    Bengio, Y
    Delalleau, O
    Le Roux, N
    Paiement, JF
    Vincent, P
    Ouimet, M
    [J]. NEURAL COMPUTATION, 2004, 16 (10) : 2197 - 2219
  • [3] A non-local algorithm for image denoising
    Buades, A
    Coll, B
    Morel, JM
    [J]. 2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2005, : 60 - 65
  • [4] EM procedures using mean field-like approximations for Markov model-based image segmentation
    Celeux, G
    Forbes, F
    Peyrard, N
    [J]. PATTERN RECOGNITION, 2003, 36 (01) : 131 - 144
  • [5] Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications
    Chen, CW
    Luo, JB
    Parker, KJ
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998, 7 (12) : 1673 - 1683
  • [6] Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure
    Chen, SC
    Zhang, DQ
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (04): : 1907 - 1916
  • [7] Multiscale image segmentation using wavelet-domain hidden Markov models
    Choi, H
    Baraniuk, RG
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2001, 10 (09) : 1309 - 1321
  • [8] Chung F., 1992, Spectral Graph Theory
  • [9] Dhillon I. S., 2004, P 10 ACM SIGKDD INT, P551, DOI DOI 10.1145/1014052.1014118
  • [10] Dhillon IS, 2007, IEEE T PATTERN ANAL, V29, P1944, DOI 10.1109/TP'AMI.2007.1115