A Dirichlet process mixture model for brain MRI tissue classification

Medical Image Analysis

Volumn 11, Issue 2, 2007, Pages 169-182

  Ferreira da Silva, Adelino R ^a  

^a UNIVERSIDADE NOVA DE LISBOA   (Portugal)

Author keywords

Automatic brain MRI classification; Dirichlet process mixture model; Magnetic resonance imaging; Nonparametric Bayesian estimation; Robust estimation

Indexed keywords

BAYESIAN NETWORKS; BRAIN; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; TISSUE;

DIRICHLET PROCESS MIXTURE MODEL; TISSUE CLASSIFICATION;

MEDICAL IMAGING;

ACCURACY; ALGORITHM; ARTICLE; BAYES THEOREM; CLASSIFICATION; CLUSTER ANALYSIS; COMPUTER PROGRAM; CONTROLLED STUDY; DIGITAL IMAGING; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; NEUROIMAGING; NONPARAMETRIC TEST; NORMAL DISTRIBUTION; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PARAMETRIC TEST; PHANTOM; PRIORITY JOURNAL; PROBABILITY; SIMULATION; VALIDATION STUDY;

BAYES THEOREM; BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, THEORETICAL;

EID: 33847607074     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2006.12.002     Document Type: Article

References (54)

1. Antoniak C.E. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics 2 6 (1974) 1152-1174
2. Ashburner J., and Friston K.J. Voxel-based morphometry - the methods. NeuroImage 11 (2000) 805-821
3. Besag J. On the statistical analysis of dirty pictures (with discussion). Journal of Royal Statistical Society B 48 3 (1986) 259-302
4. Blackwell D., and MacQueen J.B. Ferguson distributions via Polya urn schemes. The Annals of Statistics 1 2 (1973) 353-355
5. Bush C.A., and MacEachern S.N. A semiparametric Bayesian model for randomised block designs. Biometrika 83 2 (1996) 275-285
6. Casella, G., Robert, C.P., Wells, M.T., 2000. Mixture models, latent variables and partitioned importance sampling. Technical Report DT-2000-03, CEREMADE, Universite Paris Dauphine.
7. Cocosco C.A., Zijdenbos A.P., and Evans A.C. A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis 7 (2003) 513-527
8. Collins D.L., Zijdenbos A.P., Kollokian V., Sled J.G., Kabani N.J., Holmes C.J., and Evans A.C. Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging 17 3 (1998) 463-468
9. Cuadra M.B., Cammoun L., Butz T., Cuisenaire O., and Thiran J.-P. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Transactions on Medical Imaging 12 24 (2005) 1548-1565
10. Dale A.M., and Sereno M.I. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. Journal of Cognitive Neuroscience 5 (1993) 162-176
11. DeCarli C., Moisog J., Murphy D.G.M., Teichberg D., Rapoport S.I., and Horwitz B. Method of quantification of brain, ventricular, and sub-arachnoid csf volumes for MRI images. JCT 16 2 (1992) 274-284
12. Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B 39 (1977) 1-38
13. Diebolt J., and Robert C.P. Estimation of finite mixture distributions through Bayesian sampling. Journal of the Royal Statistical Society B 56 (1994) 363-375
14. Escobar M.D. Estimating normal means with a Dirichlet process prior. Journal of the American Statistical Association 89 425 (1994) 268-277
15. Escobar M.D., and West M. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90 430 (1995) 577-588
16. Ferguson T.S. A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1 2 (1973) 209-230
17. Ferreira da Silva A.F. Bayesian wavelet denoising and evolutionary calibration. Digital Signal Processing 14 (2004) 566-589
18. Frackowiak R.S.J., Friston K.J., Frith C., Dolan R., Price C.J., Zeki S., Ashburner J., and Penny W.D. Human Brain Function. second ed. (2003), Academic Press
19. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Bayesian Data Analysis. second ed. (2004), Chapman & Hall/CRC
20. Geman S., and Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (1984) 721-741
21. Grabowski T.J., Frank R.J., Szumskiand N.R., Brown C.K., and Damasio H. Validation of partial tissue segmentation of single-channel magnetic resonance images of the brain. NeuroImage 12 (2000) 640-656
22. Guillemaud R., and Brady M. Estimating the bias field of MR images. IEEE Transactions on Medical Imaging 16 3 (1997) 238-251
23. Hartigan J.A. Clustering Algorithms (1975), Wiley
24. Hassouna M.S., Farag A.A., Hushek S., and Moriarty T. Cerebrovascular segmentation from TOF using stochastic models. Medical Image Analysis 10 February (2006) 2-18
25. Heindel W.C., Jernigan T.L., Archibald S.L., Achim C.L., Masliah E., and Wiley C.A. The relationship of quantitative brain magnetic resonance imaging measures to neuropathologic indexes of human immunodeficiency virus infection. Archives of Neurology 51 (1994) 1129-1135
26. Held K., Kops E.R., Krause B.J., Wells W.M., Kikinis R., and Müller-Gärtner H. Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging 16 6 (1997) 878-886
27. Ibáñez L., and Schroeder W. The ITK Software Guide: The Insight Segmentation and Registration Toolkit (2003), Kitware, Inc., Albany, NY. http://www.itk.org/ URL
28. Kennedy D.N., Filipek P.A., and Caviness V.S. Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. IEEE Transactions on Medical Imaging 8 1 (1989) 1-7
29. Leemput K.V., Maes F., Vandermeulen D., and Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18 10 (1999) 897-908
30. MacEachern S.N., Müller P., and Neal R.M. Estimating mixture of Dirichlet process models. Journal of Computational and Graphical Statistics 7 (1998) 223-238
31. Marin J.M., Mengersen K., and Robert C.P. Bayesian modelling and inference on mixtures of distributions. Handbook of Statistics vol. 25 (2005), Elsevier-Sciences 459-507
32. Marroquin J.L., Vemuri B.C., Botello S., Calderon F., and Fernandez-Bouzas A. An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Transactions on Medical Imaging 21 8 (2002) 934-945
33. Molina V., Reig S., Sanz J., Benito C., Pascau J., Collazos F., Sarramea F., Artaloytia J.F., Gispert J.D., Luque R., Palomo T., Arango C., and Desco M. Association between relative temporal and prefrontal sulcal cerebrospinal fluid and illness duration in schizophrenia. Schizophrenia Research 58 (2002) 305-312
34. Neal R.M. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9 (2000) 249-265
35. Parry, A., Matthews, P.M., 2002. Functional magnetic resonance imaging (fMRI): A "window" into the brain. Technical Report, Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Clinical Neurology, The John Radcliffe Hospital, University of Oxford.
36. Prastawa M., Gilmore J.H., Lin W., and Gerig G. Automatic segmentation of MR images of the developing newborn brain. Medical Image Analysis 9 October (2005) 457-466
37. Richardson S., and Green P.J. On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society B 59 (1997) 731-792
38. Robert C.P. Inference in mixture models. Markov Chain Monte Carlo in Practice (1996), Chapman and Hall 441-464
39. Robert C.P., and Casella G. Monte Carlo Statistical Methods. second ed. Springer Texts in Statistics (2004), Springer-Verlag, New York
40. Ruan S., Jaggi C., Xue J., Fadili J., and Bloyet D. Brain tissue classification of magnetic resonance images using partial volume modeling. IEEE Transactions on Medical Imaging 19 12 (2000) 1179-1187
41. Santago P., and Gage H.D. Quantification of MRI brain images by mixture density and partial volume modeling. IEEE Transactions on Medical Imaging 12 3 (1993) 566-574
42. Sled J.G., Zijdenbos A.P., and Evans A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17 1 (1998) 87-97
43. Solo V., Purdon P., Weisskoff R., and Brown E. A signal estimation approach to functional MRI. IEEE Transactions on Medical Imaging 20 1 (2001) 26-35
44. Stanford, D.C., 1999. Fast automatic unsupervised image segmentation and curve detection in spatial point patterns. Ph.D. thesis, Statistics Department, University of Washington.
45. Stephens, M., 1997. Bayesian methods for mixtures of normal distributions. Ph.D. thesis, Department of Statistics, University of Oxford.
46. Titterington D.M., Smith A.F.M. and Makov U.E. Statistical Analysis of Finite Mixture Distributions (1985), Wiley, New York
47. Venables W.N., and Ripley B.D. Modern Applied Statistics with S-PLUS. second ed. Statistics and Computing (1997), Springer-Verlag, New York
48. Walker S.G., Damien P., Laud P.W., and Smith A.F.M. Bayesian nonparametric inference for random distributions and related functions. Journal of the Royal Statistical Society B 61 Part 3 (1999) 485-527
49. Wells III W.M., Grimson W.E.L., Kikinis R., and Jolesz F.A. Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15 4 (1996) 429-442
50. West, M., 1992. Hyperparameter estimation in Dirichlet process mixture models. Technical Report, Institute of Statistics and Decision Science, Duke University.
51. West M. Modeling with mixtures (with discussion). In: Bernardo J.M. (Ed). Bayesian Statistics vol. 4 (1992), Oxford University Press 503-524
52. Winkler G. Image analysis random fields and Markov Chain Monte Carlo methods. Applications of Mathematics. second ed. vol. 27 (2003), Springer-Verlag
53. Zhang Y., Brady M., and Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20 1 (2001) 45-57
54. Zijdenbos A.P., Dawant B.M., Margolin R.A., and Palmer A.C. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Transactions on Medical Imaging 13 4 (1994) 716-724