Automatic Segmentation of Putamen from Brain MRI

  • Yihui Liu
  • Bai Li
  • Dave Elliman
  • Paul Simon Morgan
  • Dorothee Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


In this paper we present an automatic segmentation of the Putamen shape from brain MRI based on wavelets and a neural network. Firstly we detect the Putamen region slice by slice using 1D wavelet feature extraction. Then fuzzy c-means technology is combined with edge detection to segment the objects inside the Putamen region. Finally features are extracted from the segmented objects and fed into a neural network classifier in order to identify the Putamen shape. Experiment shows the segmentation results to be accurate and efficient.


Brain Magnetic Resonance Image Automatic Segmentation Morphological Operation Canny Edge Detection Image Segmentation Technique 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    DeCarli, C., Murphy, D.G.M., McIntosh, A.R., Teichberg, D., Shapiro, M.B., Horwitz, B.: Discriminant Analysis of MRI Measures as A Method to Determine the Presence of De-mentia of the Alzheimer Type. Psychiatry Res. 57(2), 119–130 (1995)CrossRefGoogle Scholar
  2. 2.
    Zavaljevski, A., Dhawan, A.P., Gaskil, M., Ball, W., Johnson, J.D.: Multi-Level Adaptive Segmentation of Multi-Parameter MR Brain Images. Comput Med. Imag. Graphics 24(2), 87–98 (2000)CrossRefGoogle Scholar
  3. 3.
    Suckling, J., Sesson, T., Greenwood, K., Bullmore, E.T.: A Modified Fuzzy Clustering: Algorithm for Operator Independent Brain Tissue Classification of Dual Echo MR Images. Magn. Reson. Imag. 17(7), 1065–1076 (1999)CrossRefGoogle Scholar
  4. 4.
    Alirezaire, J., Jernigan, M.E., Nahmias, C.: Automatic Segmentation of Cerebral MR Im-ages Using Artificial Neural Networks. IEEE Trans. Nucl. Sci. 45(4), 2174–2182 (1998)CrossRefGoogle Scholar
  5. 5.
    Reddick, W.E., Glass, J.O., Cook, E.N., Elkin, T.D., Deaton, R.J.: Automated Segmenta-tion and Classification of Multispectral Magnetic Resonance Images of Brain Using Artifi-cial Neural Networks. IEEE Trans. Med. Imag. 16(6), 911–918 (1997)CrossRefGoogle Scholar
  6. 6.
    Clark, M.C., Hall, L.C., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, S.: Auto-matic Tumor Segmentation Using Knowledge-Based Techniques. IEEE Trans. Med. Imag. 17(2), 187–201 (1998)CrossRefGoogle Scholar
  7. 7.
    Duta, N., Sonka, M.: Segmentation and Interpretation of MR Brain Images: An Improved Active Shape Model. IEEE Trans. Med. Imag. 17(6), 1049–1062 (1998)CrossRefGoogle Scholar
  8. 8.
    Antalek, B., Hornak, J.P., Windig, W.: Multivariate Image Analysis of Magnetic Reso-nance Images with the Direct Exponential Curve Resolution Algorithm (DECRA): Part 2. Application to Human Brain Images. Journal of Magnetic Resonance 132(2), 307–315 (1998)CrossRefGoogle Scholar
  9. 9.
    Andersen, A.H., Zhang, Z., Avison, M.J., Gash, D.M.: Automated Segmentation of Mul-tispectral Brain MR Images. Journal of Neuroscience Methods 122(1), 13–23 (2002)CrossRefGoogle Scholar
  10. 10.
    Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR Image Segmentation Techniques Us-ing Pattern Recognition. Med. Phys. 20(4), 1033–1048 (1993)CrossRefGoogle Scholar
  11. 11.
    Amini, L., Soltanian-Zadeh, H., Lucas, C., Gity, M.: Automatic Segmentation of Thalamus from Brain MRI Integrating Fuzzy Clustering and Dynamic Contours. IEEE Transactions on Biomedical Engineering 51(5), 800–811 (2004)CrossRefGoogle Scholar
  12. 12.
    Moller, M.F.: A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks 6(4), 525–533 (1993)CrossRefGoogle Scholar
  13. 13.
    Styner, M., Brechbuhler, C., Szckely, G., Gerig, G.: Parametric Estimate of Intensity In-homogeneities Applied to MRI. IEEE Trans. Med. Imaging. 19(3), 153–165 (2000)CrossRefGoogle Scholar
  14. 14.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yihui Liu
    • 1
    • 3
  • Bai Li
    • 1
  • Dave Elliman
    • 1
  • Paul Simon Morgan
    • 2
  • Dorothee Auer
    • 2
  1. 1.School of Computer Science & ITUniversity of NottinghamNottinghamUK
  2. 2.Academic RadiologyUniversity of Nottingham, Queen’s Medical CentreNottinghamUK
  3. 3.School of Computer ScienceShandong University of Light IndustryJinanChina

Personalised recommendations