Non-local spatial spectral clustering for image segmentation

Neurocomputing

Volumn 74, Issue 1-3, 2010, Pages 461-471

  Liu, H Q ^a   Jiao, L C ^a   Zhao, F ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Image segmentation; Magnetic resonance (MR) image; Non local spatial information; Spectral clustering; Weighted kernel k means

Indexed keywords

DATA SETS; EFFECTIVE PERFORMANCE; EIGENVECTORS; IMAGING ARTIFACTS; KERNEL K-MEANS; LAPLACIAN MATRICES; MATRIX; NON-LOCAL SPATIAL INFORMATION; NONLOCAL; NORMALIZED CUTS; NORMALIZED LAPLACIAN; NOVEL ALGORITHM; OBJECTIVE FUNCTIONS; REAL IMAGES; SEGMENTATION ALGORITHMS; SEGMENTATION RESULTS; SPATIAL CONSTRAINTS; SPATIAL INFORMATIONS; SPATIAL MATRIX; SPECTRAL CLUSTERING; WEIGHTED KERNEL K-MEANS;

FUNCTIONS; IMAGE SEGMENTATION; LAPLACE TRANSFORMS; MAGNETIC RESONANCE; MATRIX ALGEBRA;

CLUSTERING ALGORITHMS;

ARTICLE; ARTIFACT; CLUSTER ANALYSIS; COMPUTER PROGRAM; IMAGE ANALYSIS; IMAGE PROCESSING; INFORMATION PROCESSING; KERNEL METHOD; MATHEMATICAL ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL;

EID: 78649453053     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.08.021     Document Type: Article

References (26)

1. 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.
2. Bengio Y., Delalleau O., Le Roux N., Paiement J.F., Vincent P., Ouimet M. Learning eigenfunctions links spectral embedding and kernel PCA. Neural Computing 2004, 16(10):2197-2219.
3. A. Buades, B. Coll, J.M. Morel, A non-local algorithm for image denoising, in: Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, San Diego, 2005, pp. 60-65.
4. Celeux G., Forbes F., Peyrard N. EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognition 2003, 36(1):131-144.
5. Chen C.W., Luo J., Parker K.J. Image segmentation via adaptive k-means clustering and knowledge-based morphological operations with biomedical applications. IEEE Transactions on Image Processing 1998, 7(12):1673-1683.
6. 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.
7. Choi H., Baraniuk R.G. Multiscale image segmentation using wavelet-domain hidden Markov models. IEEE Transactions on Image Processing 2001, 10(9):1309-1321.
8. Chung F.R.K. Spectral Graph Theory 1997, American Mathematical Society.
9. Dhillon I.S., Guan Y., Kulis B. Weighted graph cuts without eigenvectors: a multilevel approach. IEEE Transaction on Pattern Analysis and Machine Intelligence 2007, 29(11):1944-1957.
10. I.S. Dhillon, Y. Guan, B. Kulis, Kernel k-means, spectral clustering and normalized cuts, in: Proceeding of the 10th ACM Knowledge Discovery and Data Mining Conference, 2004, pp. 551-556.
11. Filippone M., Camastra F., Masulli F., Rovetta S. A survey of kernel and spectral methods for clustering. Pattern Recognition 2008, 41(1):176-190.
12. Fowlkes C., Belongie S., Chung F., Malik J. Spectral grouping using the Nyström method. IEEE Transaction on Pattern Analysis and Machine Intelligence 2004, 26(2):214-225.
13. Frank Y.S., Cheng S. Automatic seeded region growing for color image segmentation. Image and Vision Computing 2005, 23(10):877-886.
14. Ng A.Y., Jordan M.I., Weiss Y. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 2002, 14:849-856. MIT Press, USA.
15. Oliveira P.R., Romero R.A.F. Improvements on ICA mixture models for image pre-processing and segmentation. Neurocomputing 2008, 71(10-12):2180-2193.
16. Rezaee M.R., Van der Zwet P.M.J., Lelieveldt B.P.E., Van der Geest R.J., Reiber J.H.C. A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Transactions on Image Processing 2000, 9(7):1238-1248.
17. Schölkopf B., Smola A., Müller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computing 1998, 10(5):1299-1319.
18. Shapiro L.G., Stockman G.C. Computer Vision 2001, Prentice-Hall, New Jersey.
19. Shi J.B., Malik J. Normalized cuts and image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 2000, 22(8):888-905.
20. Udupa J.K., Samarasekera S. Fuzzy connectedness and object definition: theory, algorithm and applications in image segmentation. Graphical Models and Image Processing 1996, 58(3):246-261.
21. Wan S.Y., Higgins W.E. Symmetric region growing. IEEE Transactions on Image Processing 2003, 12(9):1007-1015.
22. Wang Z.M., Soh Y.C., Song Q., Sim K. Adaptive spatial information-theoretic clustering for image segmentation. Pattern Recognition 2009, 42(9):2029-2044.
23. Yang X., Krishnan S.M. Image segmentation using finite mixtures and spatial information. Image and Vision Computing 2004, 22(9):735-745.
24. Zelnik-Manor L., Perona P. Self-tuning spectral clustering. Advances in Neural Information Processing Systems 2004, 17:1601-1608. MIT Press, Vancouver, Canada, USA.
25. F. Zhao, L.C. Jiao, H.Q. Liu, Fuzzy c-means clustering with non local spatial information for noisy image segmentation, Frontiers of Computer Science in China, in press.
26. Zhou H.Y., Yuan Y., Lin F.Q., Liu T.W. Level set image segmentation with Bayesian analysis. Neurocomputing 2008, 71(10-12):1994-2000.