Skip to main content
Log in

Gaussian mixture model based segmentation methods for brain MRI images

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amato U, Larobina M, Antoniadis A, Alfano B (2003) Segmentation of magnetic resonance brain images through discriminant analysis. J Neurosci Methods 131: 65–74

    Article  Google Scholar 

  • Anbeek P, Vincken K, GS GvB, Osch Mv, Grond Jvd (2005) Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 27: 795–804

    Article  Google Scholar 

  • Ashburner J, Friston K (2005) Unified segmentation. Neuroimage 26: 839–851

    Article  Google Scholar 

  • Aubert-Broche B, Evans A, Collins L (2006) A new improved version of the realistic digital brain phantom. Neuroimage 32: 138–145

    Article  Google Scholar 

  • 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: 1410–1416

    Article  Google Scholar 

  • Awate SP, Hui Z, Gee JC (2007) A fuzzy, nonparametric segmentation framework for DTI and MRI analysis. IEEE Trans Med Imaging 26: 1525–1536

    Article  Google Scholar 

  • Balafar MA (2011a) Spatial based Expectation Maximizing (EM). Diagn Pathol 6:103

    Google Scholar 

  • Balafar MA (2011b) New spatial based MRI image de-noising algorithm. Artif Intell Rev doi:10.1007/s10462-011-9268-0

  • Balafar MA, Ramli AR, Saripan MI, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33: 261–274

    Article  Google Scholar 

  • Balafar MA, Ramli AR, Mashohor S (2010) A new method for MR grayscale inhomogeneity correction. Artif Intell Rev 34: 195–204

    Article  Google Scholar 

  • 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: 1–14

    Article  Google Scholar 

  • 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: 203–214

    Article  Google Scholar 

  • Balafar MA, Ramli AR, Mashohor S (2011) Brain magnetic resonance image segmentation using novel improvement for expectation maximizing. Neurosciences 16: 242–247

    Google Scholar 

  • Ballester MG, Zisserman A, Brady M (2002) Estimation of the partial volume effect in MRI. Med Image Anal 6: 389–405

    Article  Google Scholar 

  • Bezdek J, Hall L, Clarke L (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20: 1033–1048

    Article  Google Scholar 

  • Bricq S, Collet C, Armspach J-P (2008) Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains. Med Image Anal 12: 639–652

    Article  Google Scholar 

  • Clarke L, Velthuizen R, Phuphanich S, Schellenberg J, Arrington J, Silbiger M (1993) MRI: stability of three supervised segmentation techniques. Magn Reson Imaging 11: 95–106

    Article  Google Scholar 

  • Chellappa, R, Jain, A (eds) (1993) Markov random fields theory and application. Academic Press, London

    Google Scholar 

  • Duda RO, Hart PE (1973) Patten classification and scene analysis. Wiley-Interscience, London

    Google Scholar 

  • Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6: 721–741

    Article  MATH  Google Scholar 

  • Greenspan H, Ruf A, Goldberger J (2006) Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans Med Imaging 25: 1233–1245

    Article  Google Scholar 

  • 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: 878–886

    Article  Google Scholar 

  • Jain A, Duin R, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22: 4–37

    Article  Google Scholar 

  • Jeong S, Won CS, Gray RM (2003) Histogram-based image retrieval using Gauss mixture vector quantization. Presented at IEEE ICASSP

  • Jeong S, Won CS, Gray RM (2004) Image retrieval using color histograms generated by Gauss mixture vector quantization. Comput Vis Image Underst 94: 44–66

    Article  Google Scholar 

  • 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: 897–908

    Article  Google Scholar 

  • 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: 894–905

    Article  Google Scholar 

  • 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: 105–119

    Article  Google Scholar 

  • Li SZ (1995) Markov random field modeling in computer vision. Springer, London

    Book  Google Scholar 

  • 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: 1686–1692

    Article  Google Scholar 

  • Liang Z, Wang S (2009) An EM approach to MAP solution of segmenting tissue mixtures: a numerical analysis. IEEE Trans Med Imaging 28: 297–310

    Article  Google Scholar 

  • 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: 934–945

    Article  Google Scholar 

  • Marroquin J, Santana E, Botello S (2003) Hidden Markov measure field models for image segmentation. IEEE Trans Pattern Anal Mach Intell 25: 1380–1387

    Article  Google Scholar 

  • 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: 2544–2559

    Article  MATH  Google Scholar 

  • Ng S-K, McLachlan G (2004) Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recogn 37: 1573–1589

    Article  MATH  Google Scholar 

  • Pan Z, Lu J (2007) A Bayes-based region-growing algorithm for medical image segmentation. Comput Sci Eng 9: 32–38

    Article  Google Scholar 

  • Pham D, Xu C, Prince J (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2: 315–337

    Article  Google Scholar 

  • Rajapakse JC, Giedd JN, Rapoport JL (1997) Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans Med Imaging 16: 176–186

    Article  Google Scholar 

  • 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: 1179–1187

    Article  Google Scholar 

  • 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: 172–186

    Article  Google Scholar 

  • Scherrer B, Forbes F, Garbay C, Dojat M (2009) Distributed local MRF models for tissue and structure brain segmentation. IEEE Trans Med Imaging 28: 1278–1295

    Article  Google Scholar 

  • Silva ARFd (2007) A Dirichlet process mixture model for brain MRI tissue classification. Med Image Anal 11: 169–182

    Article  Google Scholar 

  • Silva ARFd (2009) Bayesian mixture models of variable dimension for image segmentation. comput Methods Program Biomed 94: 1–14

    Article  Google Scholar 

  • 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: 1424–1432

    Article  Google Scholar 

  • Tohka J, Zijdenbos A, Evans A (2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23: 84–97

    Article  Google Scholar 

  • Tohka J, Dinov ID, Shattuck DW, Toga AW (2010) Brain MRI tissue classification based on local Markov random fields. Magn Reson Imaging 28: 557–573

    Article  Google Scholar 

  • Wang J (2007) Discriminative Gaussian mixtures for interactive image segmentation. In: Presented at IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 386–396

  • Wells WM, Grimson WEL, Kikins R, Jolesz FA (1996) Adaptive segmentation of MRI data. IEEE Trans Med Image 15: 429–442

    Article  Google Scholar 

  • Woolrich MW, Behrens TE (2006) Variational bayes inference of spatial mixture models for segmentation. IEEE Trans Med imaging 25: 1380–1391

    Article  Google Scholar 

  • 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: 855–866

    Article  Google Scholar 

  • Zijdenbos AP, Dawant BM (1994) Brain segmentation and white matter lesion detection in MR images. Crit Rev Biomed Eng 22: 401–465

    Google Scholar 

  • 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: 45–57

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. A. Balafar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Balafar, M.A. Gaussian mixture model based segmentation methods for brain MRI images. Artif Intell Rev 41, 429–439 (2014). https://doi.org/10.1007/s10462-012-9317-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-012-9317-3

Keywords

Navigation