A Robust Statistical Method for Brain Magnetic Resonance Image Segmentation

  • Bo Qin
  • JingHua Wen
  • Ming Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper, a robust statistical model-based brain MRI image segmentation method is presented. The MRI images are modeled by Gaussian mixture model. This method, based on the statistical model, approximately finds the maximum a posteriori estimation of the segmentation and estimates the model parameters from the image data. The proposed strategy for segmentation is based on the EM and FCM algorithm. The prior model parameters are estimated via EM algorithm. Then, in order to obtain a good segmentation and speed up the convergence rate, initial estimates of the parameters were done by FCM algorithm. The proposed image segmentation methods have been tested using phantom simulated MRI data. The experimental results show the proposed method is effective and robust.


Gray Matter Gaussian Mixture Model Segmentation Result Magnetic Resonance Image Image Brain Magnetic Resonance Image Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (2000)CrossRefGoogle Scholar
  2. 2.
    Clark, M.C., Hall, L.O., Goldgof, D.B.: MRI segmentation using fuzzy clustering techniques: integrating knowledge. IEEE Eng. Med. Biol. 13(5), 730–742 (1994)CrossRefGoogle Scholar
  3. 3.
    Ozkan, M., Dawant, B.M.: Neural-Network Based Segmentation of Multi-Modal Medical Images. IEEE Transaction on Medical Imaging 12, 534–544 (1993)CrossRefGoogle Scholar
  4. 4.
    Kapur, T., Grimson, W.E., Wells, W.M., Kikinis, R.: Segmentation of brain tissue from magnetic resonance images. Med Image Anal. 1, 109–127 (1996)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Adali, T.: Quantification and segmentation of brain tissues from MR images: A probabilistic neural network approach. IEEE Trans. on Image Processing 7, 1165–1180 (1998)CrossRefGoogle Scholar
  6. 6.
    Tsai, C., Manjunath, B.S., Jagadeesan, R.: Automated segmentation of brain MR images. Pattern Recogn 28, 1825–1862 (1995)CrossRefGoogle Scholar
  7. 7.
    Wells, W.M., Grimson, W.E.L.: Adaptive Segmentation of MRI data. IEEE Transaction on Medical Imaging 15, 429–442 (1996)CrossRefGoogle Scholar
  8. 8.
    Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classiffication of MR images of the brain. IEEE trans. on medical imaging 18, 897–908 (1999)CrossRefGoogle Scholar
  9. 9.
    McLachlan, G.J., Krishnan, T.: The EM algorithm and extensions. John Wiley and Sons, New York (1996)Google Scholar
  10. 10.
    McLachlan, G.M., Peel, D.: Finite Mixture Models. John Wiley & Sons, Inc., New York (2001)Google Scholar
  11. 11.
    Lorette, A., Descombes, X., Zerubia, J.: Urban aereas extraction based on texture analysis through a markovian modeling. International journal of computer vision 36, 219–234 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bo Qin
    • 1
  • JingHua Wen
    • 2
  • Ming Chen
    • 1
  1. 1.Department of Automatic Control of Northwestern Polytechnical UniversityXi’anP.R. China
  2. 2.School of Medicine of Xi’an JiaoTong UniversityXi’anP.R. China

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