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Volumn 36, Issue 3, 2015, Pages 185-196

Automated MRI brain tissue segmentation based on mean shift and fuzzy c -means using a priori tissue probability maps

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE MEAN SHIFT; ARTICLE; AUTOMATION; BAYES THEOREM; BAYESIAN ADAPTIVE MEAN SHIFT; BRAIN MAPPING; CEREBROSPINAL FLUID; FUZZY C MEAN; FUZZY SYSTEM; GRAY MATTER; HUMAN; KERNEL METHOD; NEUROIMAGING; NOISE; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION; PROBABILITY; WHITE MATTER;

EID: 84929837828     PISSN: 19590318     EISSN: 18760988     Source Type: Journal    
DOI: 10.1016/j.irbm.2015.01.007     Document Type: Article
Times cited : (40)

References (41)
  • 1
    • 0033837078 scopus 로고    scopus 로고
    • Normal brain development and aging: Quantitative analysis at in vivo MR imaging in healthy volunteers
    • Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 2000;3:672-82.
    • (2000) Radiology , vol.3 , pp. 672-682
    • Courchesne, E.1    Chisum, H.J.2    Townsend, J.3    Cowles, A.4    Covington, J.5    Egaas, B.6
  • 3
    • 84862840271 scopus 로고    scopus 로고
    • Segmentation of multiple sclerosis lesions in MR images: A review
    • Daryoush M, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology 2012;54:299-332.
    • (2012) Neuroradiology , vol.54 , pp. 299-332
    • Daryoush, M.1    Kouzani, A.Z.2    Soltanian-Zadeh, H.3
  • 4
    • 56349133955 scopus 로고    scopus 로고
    • EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model
    • Rullmann M, Anwander A, Dannhauer M, Warfield S K , Duffy FH, Wolters CH. EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model. NeuroImage 2009;44:399-410.
    • (2009) NeuroImage , vol.44 , pp. 399-410
    • Rullmann, M.1    Anwander, A.2    Dannhauer, M.3    Warfield, S.K.4    Duffy, F.H.5    Ch, W.6
  • 8
    • 84899897383 scopus 로고    scopus 로고
    • Automatic quantification of MS lesions in 3D MRI brain data sets: Validation of INSECT
    • Zijdenbos A, Forghani R, Evans A. Automatic quantification of MS lesions in 3D MRI brain data sets: validation of INSECT. MICCAI 1998;1496:439-48.
    • (1998) MICCAI , vol.1496 , pp. 439-448
    • Zijdenbos, A.1    Forghani, R.2    Evans, A.3
  • 9
    • 0142260969 scopus 로고    scopus 로고
    • A fully automatic and robust brain MRI tissue classification method
    • Cocosco C, Zijdenbos A, Evans A. A fully automatic and robust brain MRI tissue classification method. Med Image Anal 2003;7:513-27.
    • (2003) Med Image Anal , vol.7 , pp. 513-527
    • Cocosco, C.1    Zijdenbos, A.2    Evans, A.3
  • 11
    • 79551687036 scopus 로고    scopus 로고
    • Atlas guided identification of brain structures by combining 3D segmentation and SVM classification
    • Akselrod-Ballin A, Galun M, Gomori JM, Basri R, Brandt A. Atlas guided identification of brain structures by combining 3D segmentation and SVM classification. MICCAI 2006:209-16.
    • (2006) MICCAI , pp. 209-216
    • Akselrod-Ballin, A.1    Galun, M.2    Gomori, J.M.3    Basri, R.4    Brandt, A.5
  • 15
    • 20344379177 scopus 로고    scopus 로고
    • Improved EM-based tissue segmentation and partial volume effect quantification in multi-sequence brain MRI
    • Dugas-Phocion G, Ballester MÁG, Malandain G, Lebrun C, Ayache N. Improved EM-based tissue segmentation and partial volume effect quantification in multi-sequence brain MRI. MICCAI 2004:26-33.
    • (2004) MICCAI , pp. 26-33
    • Dugas-Phocion, G.1    Mág, B.2    Malandain, G.3    Lebrun, C.4    Ayache, N.5
  • 16
    • 33748123943 scopus 로고    scopus 로고
    • Constrained Gaussian mixture model framework for automatic segmentation of MR brain images
    • Greenspan H, Ruf A, Goldberger J. Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans Med Imaging 2006;25:1233-45.
    • (2006) IEEE Trans Med Imaging , vol.25 , pp. 1233-1245
    • Greenspan, H.1    Ruf, A.2    Goldberger, J.3
  • 17
    • 0036689044 scopus 로고    scopus 로고
    • An accurate and efficient Bayesian method for automatic segmentation of brain MRI
    • Marroquín JL, Vemuri BC, Botello S, Calderon F. An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans Med Imaging 2002;21:934-44.
    • (2002) IEEE Trans Med Imaging , vol.21 , pp. 934-944
    • Marroquín, J.L.1    Vemuri, B.C.2    Botello, S.3    Calderon, F.4
  • 18
    • 0034745001 scopus 로고    scopus 로고
    • Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
    • Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45-57.
    • (2001) IEEE Trans Med Imaging , vol.20 , pp. 45-57
    • Zhang, Y.1    Brady, M.2    Smith, S.3
  • 20
    • 77952291868 scopus 로고    scopus 로고
    • Brain MRI tissue classification based on local Markov random fields
    • Tohka J, Dinov ID, Shattuck DW, Toga AW. Brain MRI tissue classification based on local Markov random fields. Magn Reson Imaging 2010;28:557-73.
    • (2010) Magn Reson Imaging , vol.28 , pp. 557-573
    • Tohka, J.1    Dinov, I.D.2    Shattuck, D.W.3    Toga, A.W.4
  • 21
    • 68249124272 scopus 로고    scopus 로고
    • Distributed local MRF models for tissue and structure brain segmentation
    • Scherrer B, Forbes F, Garbay C, Dojat M. Distributed local MRF models for tissue and structure brain segmentation. IEEE Trans Med Imaging 2009;28:1278-95.
    • (2009) IEEE Trans Med Imaging , vol.28 , pp. 1278-1295
    • Scherrer, B.1    Forbes, F.2    Garbay, C.3    Dojat, M.4
  • 22
    • 68249111619 scopus 로고    scopus 로고
    • An adaptive mean-shift framework for MRI brain segmentation
    • Mayer A, Greenspan H. An adaptive mean-shift framework for MRI brain segmentation. IEEE Trans Med Imaging 2009;28:1238-49.
    • (2009) IEEE Trans Med Imaging , vol.28 , pp. 1238-1249
    • Mayer, A.1    Greenspan, H.2
  • 23
    • 0016421071 scopus 로고
    • The estimation of the gradient of a density function, with applications in pattern recognition
    • Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 1975;21:32-40.
    • (1975) IEEE Trans Inf Theory , vol.21 , pp. 32-40
    • Fukunaga, K.1    Hostetler, L.2
  • 24
    • 0036565814 scopus 로고    scopus 로고
    • Mean shift: A robust approach toward feature space analysis
    • Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 2002;24:603-19.
    • (2002) IEEE Trans Pattern Anal Mach Intell , vol.24 , pp. 603-619
    • Comaniciu, D.1    Meer, P.2
  • 25
    • 0037331011 scopus 로고    scopus 로고
    • An algorithm for data-driven bandwidth selection
    • Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans Pattern Anal Mach Intell 2003;25:281-8.
    • (2003) IEEE Trans Pattern Anal Mach Intell , vol.25 , pp. 281-288
    • Comaniciu, D.1
  • 26
    • 0345414073 scopus 로고    scopus 로고
    • Mean-shift based clustering in high dimensions: A texture classification example
    • Georgescu B, Shimshoni I, Meer P. Mean-shift based clustering in high dimensions: a texture classification example. ICCV 2003:456-63.
    • (2003) ICCV , pp. 456-463
    • Georgescu, B.1    Shimshoni, I.2    Meer, P.3
  • 27
    • 84929837766 scopus 로고    scopus 로고
    • Internet Brain Segmentation Repository Center for Morphometric Analysis (IBSR). http://www.cma.mgh.harvard.edu/ibsr.
  • 28
    • 84929836385 scopus 로고    scopus 로고
    • BrainWeb. http://brainweb.bic.mni.mcgill.ca/brainweb/.
  • 29
    • 70350109048 scopus 로고    scopus 로고
    • Kernel bandwidth estimation for nonparametric modeling
    • Bors AG, Nasios N. Kernel bandwidth estimation for nonparametric modeling. IEEE Trans SMC 2009;39:1543-55.
    • (2009) IEEE Trans SMC , vol.39 , pp. 1543-1555
    • Bors, A.G.1    Nasios, N.2
  • 31
    • 84929839735 scopus 로고    scopus 로고
    • International Consortium for Brain Mapping (ICBM). http://www.loni. ucla.edu/ICBM/Downloads/Downloads-ICBMprobabilistic.shtml.
  • 32
    • 84929836452 scopus 로고    scopus 로고
    • Statistical Parametric Mapping (SPM). http://www.fil.ion.ucl.ac.uk/spm/.
  • 33
    • 0031987382 scopus 로고    scopus 로고
    • A nonparametric method for automatic correction of intensity nonuniformity in MRI data
    • Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17:87-97.
    • (1998) IEEE Trans Med Imaging , vol.17 , pp. 87-97
    • Sled, J.G.1    Zijdenbos, A.P.2    Evans, A.C.3
  • 34
    • 0036425968 scopus 로고    scopus 로고
    • Improved optimisation for the robust and accurate linear registration and motion correction of brain images
    • Jenkinson M, Bannister PR, Brady JM, Smith SM. Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 2002;17:825-41.
    • (2002) NeuroImage , vol.17 , pp. 825-841
    • Jenkinson, M.1    Bannister, P.R.2    Brady, J.M.3    Smith, S.M.4
  • 35
    • 84919402534 scopus 로고    scopus 로고
    • Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations
    • Valverde S , Oliver A, Cabezas M, Roura E, Lladó X. Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations. J Magn Reson Imaging 2014:1522-2586.
    • (2014) J Magn Reson Imaging , pp. 1522-2586
    • Valverde, S.1    Oliver, A.2    Cabezas, M.3    Roura, E.4    Lladó, X.5
  • 36
    • 84895143331 scopus 로고    scopus 로고
    • Bayesian estimation of probabilistic atlas for tissue segmentation
    • Xu H, Thirion B, Allassonnière S. Bayesian estimation of probabilistic atlas for tissue segmentation. IRBM 2014;35:27-32.
    • (2014) IRBM , vol.35 , pp. 27-32
    • Xu, H.1    Thirion, B.2    Allassonnière, S.3
  • 39
    • 84929838018 scopus 로고    scopus 로고
    • BrainSuite Software. http://neuroimage.usc.edu/neuro/BrainSuite/.
  • 40
    • 0000250265 scopus 로고
    • Measures of the amount of ecologic association between species
    • Dice LR. Measures of the amount of ecologic association between species. Ecology 1945;26:297-302.
    • (1945) Ecology , vol.26 , pp. 297-302
    • Dice, L.R.1
  • 41
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • Thomas GD. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 1998;10:1895-919.
    • (1998) Neural Comput , vol.10 , pp. 1895-1919
    • Thomas, G.D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.