MRI Brain Image Segmentation with Supervised SOM and Probability-Based Clustering Method

  • Andres Ortiz
  • Juan M. Gorriz
  • Javier Ramirez
  • Diego Salas-Gonzalez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

Nowadays, the improvements in Magnetic Resonance Imaging systems (MRI) provide new and aditional ways to diagnose some brain disorders such as schizophrenia or the Alzheimer’s disease. One way to figure out these disorders from a MRI is through image segmentation. Image segmentation consist in partitioning an image into different regions. These regions determine diferent tissues present on the image. This results in a very interesting tool for neuroanatomical analyses. Thus, the diagnosis of some brain disorders can be figured out by analyzing the segmented image. In this paper we present a segmentation method based on a supervised version of the Self-Organizing Maps (SOM). Moreover, a probability-based clustering method is presented in order to improve the resolution of the segmented image. On the other hand, the comparisons with other methods carried out using the IBSR database, show that our method ourperforms other algorithms.

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References

  1. 1.
    Kapur, T., Grimson, W., Wells, I., Kikinis, R.: Segmentation of brain tissue from magnetic resonance images. Medical Image Analysis 1(2), 109–127 (1996)CrossRefGoogle Scholar
  2. 2.
    Kennedy, D., Filipek, P., Caviness, V.: Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. IEEE Transactions on Medical Imaging 8(1), 1–7 (1989)CrossRefGoogle Scholar
  3. 3.
    Smith, S., Brady, M., Zhang, Y.: Segmentation of brain images through a hidden Markov Random Field Model and the Expectation-Maximization Algorithm. IEEE Transactions on Medical Imaging 20(1) (2001)Google Scholar
  4. 4.
    Yang, Z., Laaksonen, J.: Interactive Retrieval in Facial Image Database Using Self-Organizing Maps. In: MVA (2005)Google Scholar
  5. 5.
    Tsai, Y., Chiang, I., Lee, Y., Liao, C., Wang, K.: Automatic MRI Meningioma Segmentation Using Estimation Maximization. In: Proceedings of the 27th IEEE Engineering in Medicine and Biology Annual Conference (2005)Google Scholar
  6. 6.
    Xie, J., Tsui, H.: Image Segmentation based on maximum-likelihood estimation and optimum entropy distribution (MLE-OED). Pattern Recognition Letters 25, 1133–1141 (2005)CrossRefGoogle Scholar
  7. 7.
    Smith, S., Brady, M., Zhang, Y.: Segmentation of brain images through a hidden Markov Random Field Model and the Expectation-Maximization Algorithm. IEEE Transactions on Medical Imaging 20(1) (2001)Google Scholar
  8. 8.
    Wells, W., Grimson, W., Kikinis, R., Jolesz, F.: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15(4), 429–442 (1996)CrossRefGoogle Scholar
  9. 9.
    Mohamed, N., Ahmed, M., Farag, A.: Modified fuzzy c-mean in medical image segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (1999)Google Scholar
  10. 10.
    Parra, C., Iftekharuddin, K., Kozma, R.: Automated Brain Tumor Segmentation and Pattern recognition using AAN. In: Computational Intelligence, Robotics and Autonomous SystemsGoogle Scholar
  11. 11.
    Sahoo, P., Soltani, S., Wong, A., Chen, Y.: A survey of thresholding techniques. Computer Vision, Graphics Image Process. 41, 233–260Google Scholar
  12. 12.
    Yang, Z., Laaksonen, J.: Interactive Retrieval in Facial Image Database Using Self-Organizing Maps. In: MVA (2005)Google Scholar
  13. 13.
    Güler, I., Demirhan, A., Karakis, R.: Interpretation of MR images using self-organizing maps and knowledge-based expert systems. Digital Signal Processing 19, 668–677 (2009)CrossRefGoogle Scholar
  14. 14.
    Ong, S., Yeo, N., Lee, K., Venkatesh, Y., Cao, D.: Segmentation of color images using a two-stage self-organizing network. Image and Vision Computing 20, 279–289 (2002)CrossRefGoogle Scholar
  15. 15.
    Alirezaie, J., Jernigan, M., Nahmias, C.: Automatic segmentation of cerebral MR images using artificial neural Networks. IEEE Transactions on Nuclear Science 45(4), 2174–2182 (1998)CrossRefGoogle Scholar
  16. 16.
    Sun, W.: Segmentation method of MRI using fuzzy Gaussian basis neural network. Neural Information Processing 8(2), 19–24 (2005)Google Scholar
  17. 17.
    Fan, L., Tian, D.: A brain MR images segmentation method based on SOM neural network. In: IEEE International Conference on Bioinformatics and Biomedical Engineering (2007)Google Scholar
  18. 18.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)CrossRefMATHGoogle Scholar
  19. 19.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems and Cybernet. 6, 610–621 (1973)CrossRefGoogle Scholar
  20. 20.
    Greenspan, H., Ruf, A., Goldberger, J.: Constrained Gaussian Mixture Model Framework for Automatic Segmentation of MR Brain Images. IEEE Transactions on Medical Imaging 25(10), 1233–1245Google Scholar
  21. 21.
    Hodgson, M.E.: What Size Window for Image Classification? A Cognitive Perspective. Photogrammetric Engineering & Remote Sensing. American Society for Photogrammetry and Remote Sensing 64(8), 797–807Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andres Ortiz
    • 1
  • Juan M. Gorriz
    • 2
  • Javier Ramirez
    • 2
  • Diego Salas-Gonzalez
    • 2
  1. 1.Communications Engineering DepartmentUniversity of MalagaMalagaSpain
  2. 2.Department of Signal Theory, Communications and NetworkingUniversity of GranadaGranadaSpain

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