Medical Image Segmentation Using Fuzzy C-Mean (FCM), Learning Vector Quantization (LVQ) and User Interaction

  • M. A. Balafar
  • Abd. Rahman Ramli
  • M. Iqbal Saripan
  • Rozi Mahmud
  • Syamsiah Mashohor
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)


Accurate segmentation of medical images is very essential in medical applications. We proposed a new method, based on combination of Learning Vector Quantization (LVQ), FCM and user interaction to make segmentation more robust against inequality of content with semantic, low contrast, in homogeneity and noise. In the postulated method, noise is decreased using Stationary wavelet Transform (SWT); input image is clustered using FCM to the n clusters where n is the number of target classes, afterwards, user selects some of the clusters to be partitioned again; each user selected cluster is clustered to two sub clusters using FCM. This process continues until user to be satisfied. Then, user selects clusters for each target class; user selected clusters are used to train LVQ. After training LVQ, image pixels are clustered by LVQ. Segmentation of simulated and real images is demonstrated to show effectiveness of new method.


Learning Vector Quantization (LVQ) medical image segmentation user interaction 


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  1. 1.
    Brain Web [Online],
  2. 2.
    Dan, T., Linan, F.: A Brain MR Images Segmentation Method Based on SOM Neural Network. In: ICBBE, pp. 686–689 (2007)Google Scholar
  3. 3.
    Pham, D.L.: Spatial Models for Fuzzy Clustering. Comput. Vis. Imag. Understand 84, 285–297 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Zhang, D.Q., Chen, S.C.: A novel Kernelized Fuzzy C-means Algorithm with Application in Medical Image Segmentation. Artif. Intell.Med. 37–52 (2004)Google Scholar
  5. 5.
    Farhang, S., Tizhoosh, H.R., Salama, M.M.A.: Application of Opposition-Based Reinforcement Learning in Image Segmentation. In: ADPRL, pp. 246–251 (2007) Google Scholar
  6. 6.
    Foued, D., Abdelmalik, T.-A., Azzeddine, C., Fethi, B.-R.: MR Images Segmentation Based on Coupled Geometrical Active Contour Model to Anisotropic Diffusion Filtering. In: ICBBE, pp. 721–724 (2007)Google Scholar
  7. 7.
    Yu, J.-H., Wang, Y.-Y., Chen, P., Xu, H.-Y.: Two-Dimensional Fuzzy Clustering for Ultrasound Image Segmentation. In: ICBBE, pp. 599–603 (2007)Google Scholar
  8. 8.
    Bezdek, C.a., Bezek, J.C.: Pattern Recognition with Fuzzy Object Function Algorithms, Stanford Research Institute, Menlo Park. Plenum, New York (1981)Google Scholar
  9. 9.
    Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., Bezdek, J.C.: A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of The Brain. IEEE Trans. Neural Netw. 3, 672–682 (1992)CrossRefGoogle Scholar
  10. 10.
    Ceccarelli, M., De Luca, N., Morganella, A.: Automatic Measurement of the Intima-Media Thickness with Active Contour Based Image Segmentation. In: IEEE International Workshop on Medical Measurement and Applications, Sannio Univ., Benevento, pp. 1–5 (2007)Google Scholar
  11. 11.
    Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A Modified Fuzzy C-means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Trans. Med. Imag. 21, 193–199 (2002)CrossRefGoogle Scholar
  12. 12.
    Shen, S., Sandham, W., Granat, M., Sterr, A.: MRI Fuzzy Segmentation of Brain Tissue Using Neighbourhood Attraction with Neural-Network Optimization. IEEE Trans. Inform. Tech. Biomedicine 9, 459–467 (2005)CrossRefGoogle Scholar
  13. 13.
    Acton, S.T., Mukherjee, D.P.: Scale Space Classification Using Area Morphology. IEEE Trans. Image Process. 9, 623–635 (2000)CrossRefGoogle Scholar
  14. 14.
    Dave, R.N.R.N.: Characterization and Detection of Noise in Clustering. Pattern Recognit. Lett. 12, 657–664 (1991)CrossRefGoogle Scholar
  15. 15.
    Krishnapuram, R.R., Keller, J.M.: A Possibilistic Approach to Clustering, IEEE Trans. Fuzzy Syst. 1, 98–110 (1993)CrossRefGoogle Scholar
  16. 16.
    Tolias, Y.A., Panas, S.M.: On Applying Spatial Constraints in Fuzzy Image Clustering Using a Fuzzy Rule-based System. IEEE Signal. Process. Lett. 5, 245–247 (1998)CrossRefGoogle Scholar
  17. 17.
    Wells III, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A.: Adaptive Segmentation of MRI Data. IEEE Trans. Med. Imag. 15, 429–442 (1996)CrossRefGoogle Scholar
  18. 18.
    Zhang, J., Liu, J.: Image Segmentation with Multi-Scale GVF Snake Model Based on B-Spline Wavelet ACIS. pp. 259–263 (2007)Google Scholar
  19. 19.
    Coifman, R.R., Donoho, D.L.: Translation Invariant De-noising. Lecture Notes in Statistics, vol. 103, pp. 125–150. Springer, New York (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M. A. Balafar
    • 1
  • Abd. Rahman Ramli
    • 1
  • M. Iqbal Saripan
    • 1
  • Rozi Mahmud
    • 2
  • Syamsiah Mashohor
    • 1
  1. 1.Dept of Computer & Communication Systems, Faculty of EngineeringUniversity Putra MalaysiaSerdangMalaysia
  2. 2.Faculty of MedicineUniversiti Putra MalaysiaSerdangMalaysia

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