Artificial Intelligence Review

, Volume 41, Issue 3, pp 429–439 | Cite as

Gaussian mixture model based segmentation methods for brain MRI images

  • M. A. Balafar


Image segmentation is at a preliminary stage of inclusion in diagnosis tools and the accurate segmentation of brain MRI images is crucial for a correct diagnosis by these tools. Due to in-homogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A review of the Gaussian Mixture Model based segmentation algorithms for brain MRI images is presented. The review covers algorithms for segmentation algorithms and their comparative evaluations based on reported results.


Statistical MRI Brain 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amato U, Larobina M, Antoniadis A, Alfano B (2003) Segmentation of magnetic resonance brain images through discriminant analysis. J Neurosci Methods 131: 65–74CrossRefGoogle Scholar
  2. Anbeek P, Vincken K, GS GvB, Osch Mv, Grond Jvd (2005) Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 27: 795–804CrossRefGoogle Scholar
  3. Ashburner J, Friston K (2005) Unified segmentation. Neuroimage 26: 839–851CrossRefGoogle Scholar
  4. Aubert-Broche B, Evans A, Collins L (2006) A new improved version of the realistic digital brain phantom. Neuroimage 32: 138–145CrossRefGoogle Scholar
  5. Aubert-Broche B, Griffin M, Pike G, Evans A, Collins D (2006) Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans Med Imaging 25: 1410–1416CrossRefGoogle Scholar
  6. Awate SP, Hui Z, Gee JC (2007) A fuzzy, nonparametric segmentation framework for DTI and MRI analysis. IEEE Trans Med Imaging 26: 1525–1536CrossRefGoogle Scholar
  7. Balafar MA (2011a) Spatial based Expectation Maximizing (EM). Diagn Pathol 6:103Google Scholar
  8. Balafar MA (2011b) New spatial based MRI image de-noising algorithm. Artif Intell Rev doi: 10.1007/s10462-011-9268-0
  9. Balafar MA, Ramli AR, Saripan MI, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33: 261–274CrossRefGoogle Scholar
  10. Balafar MA, Ramli AR, Mashohor S (2010) A new method for MR grayscale inhomogeneity correction. Artif Intell Rev 34: 195–204CrossRefGoogle Scholar
  11. Balafar MA, Ramli AR, Saripan MI, Mashohor S, Mahmud R (2010) Medical image segmentation using fuzzy c-mean (FCM) and user specified data. J Circuits Syst Comput 19: 1–14CrossRefGoogle Scholar
  12. Balafar MA, Ramli AR, Saripan MI, Mashohor S, Mahmud R (2010) Improved fast fuzzy c-mean and its application in medical image segmentation. J Circuits Syst Comput 19: 203–214CrossRefGoogle Scholar
  13. Balafar MA, Ramli AR, Mashohor S (2011) Brain magnetic resonance image segmentation using novel improvement for expectation maximizing. Neurosciences 16: 242–247Google Scholar
  14. Ballester MG, Zisserman A, Brady M (2002) Estimation of the partial volume effect in MRI. Med Image Anal 6: 389–405CrossRefGoogle Scholar
  15. Bezdek J, Hall L, Clarke L (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20: 1033–1048CrossRefGoogle Scholar
  16. Bricq S, Collet C, Armspach J-P (2008) Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains. Med Image Anal 12: 639–652CrossRefGoogle Scholar
  17. Clarke L, Velthuizen R, Phuphanich S, Schellenberg J, Arrington J, Silbiger M (1993) MRI: stability of three supervised segmentation techniques. Magn Reson Imaging 11: 95–106CrossRefGoogle Scholar
  18. Chellappa, R, Jain, A (eds) (1993) Markov random fields theory and application. Academic Press, LondonGoogle Scholar
  19. Duda RO, Hart PE (1973) Patten classification and scene analysis. Wiley-Interscience, LondonGoogle Scholar
  20. Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6: 721–741CrossRefzbMATHGoogle Scholar
  21. Greenspan H, Ruf A, Goldberger J (2006) Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans Med Imaging 25: 1233–1245CrossRefGoogle Scholar
  22. Held K, Kops ER, Krause B, Wells WM III, Kikinis R, Müller-Gärtner H-W (1997) Markov random field segmentation of brain MR images. IEEE Trans Med Imaging 16: 878–886CrossRefGoogle Scholar
  23. Jain A, Duin R, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22: 4–37CrossRefGoogle Scholar
  24. Jeong S, Won CS, Gray RM (2003) Histogram-based image retrieval using Gauss mixture vector quantization. Presented at IEEE ICASSPGoogle Scholar
  25. Jeong S, Won CS, Gray RM (2004) Image retrieval using color histograms generated by Gauss mixture vector quantization. Comput Vis Image Underst 94: 44–66CrossRefGoogle Scholar
  26. Leemput KV, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging 18: 897–908CrossRefGoogle Scholar
  27. Lee JD, Su HR, Cheng PE, Liou M, Aston J, Tsai AC, Chen CY (2009) MR image segmentation using a power transformation approach. IEEE Trans Med Imaging 28: 894–905CrossRefGoogle Scholar
  28. Leemput KV, Maes F, Vandermeulen D, Suetens P (2003) A unifying framework for partial volume segmentation of brain MR images. IEEE Trans Med Imaging 22: 105–119CrossRefGoogle Scholar
  29. Li SZ (1995) Markov random field modeling in computer vision. Springer, LondonCrossRefGoogle Scholar
  30. Li L, Li X, Lu H, Huang W, Christodoulou C, Tudorica A, Krupp LB, Liang Z (2003) MRI volumetric analysis of multiple sclerosis: methodology and validation. IEEE Trans Nucl Sci 50: 1686–1692CrossRefGoogle Scholar
  31. Liang Z, Wang S (2009) An EM approach to MAP solution of segmenting tissue mixtures: a numerical analysis. IEEE Trans Med Imaging 28: 297–310CrossRefGoogle Scholar
  32. Marroquin J, Vemuri B, Botello S, Calderon F, Fernandez-Bouzas A (2002) An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans Med Imaging 21: 934–945CrossRefGoogle Scholar
  33. Marroquin J, Santana E, Botello S (2003) Hidden Markov measure field models for image segmentation. IEEE Trans Pattern Anal Mach Intell 25: 1380–1387CrossRefGoogle Scholar
  34. M’hiri S, Cammoun L, Ghorbel F (2007) Speeding up HMRF_EM algorithms for fast unsupervised image segmentation by Bootstrap resampling: application to the brain tissue segmentation. Signal Process 87: 2544–2559CrossRefzbMATHGoogle Scholar
  35. Ng S-K, McLachlan G (2004) Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recogn 37: 1573–1589CrossRefzbMATHGoogle Scholar
  36. Pan Z, Lu J (2007) A Bayes-based region-growing algorithm for medical image segmentation. Comput Sci Eng 9: 32–38CrossRefGoogle Scholar
  37. Pham D, Xu C, Prince J (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2: 315–337CrossRefGoogle Scholar
  38. Rajapakse JC, Giedd JN, Rapoport JL (1997) Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans Med Imaging 16: 176–186CrossRefGoogle Scholar
  39. Ruan S, Jaggi C, Xue J, Fadili J, Bloyet D (2000) Brain tissue classification of magnetic resonance images using partial volume modeling. IEEE Trans Med Imaging 19: 1179–1187CrossRefGoogle Scholar
  40. Schroeter P, Vesin JM, Langenberger T, Meuli R (1998) Robust parameter estimation of intensity distributions for brain magnetic resonance images. IEEE Trans Med imaging 17: 172–186CrossRefGoogle Scholar
  41. Scherrer B, Forbes F, Garbay C, Dojat M (2009) Distributed local MRF models for tissue and structure brain segmentation. IEEE Trans Med Imaging 28: 1278–1295CrossRefGoogle Scholar
  42. Silva ARFd (2007) A Dirichlet process mixture model for brain MRI tissue classification. Med Image Anal 11: 169–182CrossRefGoogle Scholar
  43. Silva ARFd (2009) Bayesian mixture models of variable dimension for image segmentation. comput Methods Program Biomed 94: 1–14CrossRefGoogle Scholar
  44. Song T, Jamshidi MM, Lee RR, Huang M (2007) A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Trans Neural Netw 18: 1424–1432CrossRefGoogle Scholar
  45. Tohka J, Zijdenbos A, Evans A (2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23: 84–97CrossRefGoogle Scholar
  46. Tohka J, Dinov ID, Shattuck DW, Toga AW (2010) Brain MRI tissue classification based on local Markov random fields. Magn Reson Imaging 28: 557–573CrossRefGoogle Scholar
  47. Wang J (2007) Discriminative Gaussian mixtures for interactive image segmentation. In: Presented at IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 386–396Google Scholar
  48. Wells WM, Grimson WEL, Kikins R, Jolesz FA (1996) Adaptive segmentation of MRI data. IEEE Trans Med Image 15: 429–442CrossRefGoogle Scholar
  49. Woolrich MW, Behrens TE (2006) Variational bayes inference of spatial mixture models for segmentation. IEEE Trans Med imaging 25: 1380–1391CrossRefGoogle Scholar
  50. Xue Z, Shen D, Karacali B, Stern J, Rottenberg D, Davatzikos C (2006) Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms. Neuroimage 33: 855–866CrossRefGoogle Scholar
  51. Zijdenbos AP, Dawant BM (1994) Brain segmentation and white matter lesion detection in MR images. Crit Rev Biomed Eng 22: 401–465Google Scholar
  52. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20: 45–57CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Computer, Faculty of EngineeringUniversity of TabrizTabrizIran

Personalised recommendations