Medical Image Segmentation Using Anisotropic Filter, User Interaction and Fuzzy C-Mean (FCM)

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

Abstract

We proposed a new clustering method based on Anisotropic Filter, user interaction and fuzzy c-mean (FCM). In the postulated method, the color image is converted to grey level image and anisotropic filter is applied to decrease noise; User selects training data for each target class, afterwards, the image is clustered using ordinary FCM. Due to in-homogeneity and unknown noise some clusters contain training data for more than one target class. These clusters are partitioned again. This process continues until there are not such clusters. Then, the clusters contain training data for a target class assigned to that target class; Mean of intensity in each class is considered as feature for that class, afterwards, feature distance of each unsigned cluster from different class is found then unsigned clusters are signed to target class with least distance from. Experimental result is demonstrated to show effectiveness of new method.

Keywords

Anisotropic filter medical image segmentation user interaction fuzzy c-mean (FCM) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Chang, P.L., Teng, W.G.: Exploiting the Self-Organizing Map for Medical Image Segmentation. In: CBMS, pp. 281–288 (2007)Google Scholar
  3. 3.
    Jan, J.: Medical Image Processing Reconstruction and Restoration Concepts and Methods. CRC, Taylor (2005)Google Scholar
  4. 4.
    Jiang, Y., Meng, J., Babyn, P.: X-ray Image Segmentation using Active Contour Model with Global Constraints. 240–245 (2007)Google Scholar
  5. 5.
    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(5), 672–682 (1992)CrossRefGoogle Scholar
  6. 6.
    Acton, S.T., Mukherjee, D.P.: Scale Space Classification Using Area Morphology. IEEE Trans. Image Process. 9(4), 623–635 (2000)CrossRefGoogle Scholar
  7. 7.
    Zhang, D.Q., Chen, S.C.: A Novel Kernelized Fuzzy C-means Algorithm With Application in Medical Image Segmentation. Artif. Intell. Med. 32, 37–52 (2004)CrossRefGoogle Scholar
  8. 8.
    Dave, R.N.: Characterization and Detection of Noise in Clustering. Pattern Recognit. Lett. 12, 657–664 (1991)CrossRefGoogle Scholar
  9. 9.
    Catte, F., Lions, P.L., Morel, J.M.: Col and Edge Detection by Nonlinear Diffusion.  92(12), 182–193 (1992)Google Scholar
  10. 10.
    Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. Pattern Anal. Mach. Intel. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  11. 11.
    You, Y.L., Xu, W., Tannenbaum, A., Kaveh, M.: Behavioral Analysis of Anisotropic Diffusion in Image Processing. IEEE Trans. Image Process. 5(11), 1539–1553 (1996)CrossRefGoogle Scholar
  12. 12.
    Pan, Z.G., Lu, J.F.: A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation. IEEE Computing in Science & Engineering 9(4), 32–38 (2007)MathSciNetGoogle Scholar
  13. 13.
    Cattle, F., Coll, T., Lions, P.L., Morel, J.M.: Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. SIAM J. Number. Anal. 92(12), 182–193 (1992)CrossRefGoogle Scholar
  14. 14.
    Ren, J.J., He, M.Y.: A Level Set Method for Image Segmentation by Integrating Channel Anisotropic Diffusion Information. Second IEEE conf. IEA. pp. 2554–2557 (2007) Google Scholar
  15. 15.
    Pohle, R., Toennies, L.D.: Segmentation of Medical Images using Adaptive Region Growing. Proc. SPIE, Medical Imaging. 4322 (2001)Google Scholar
  16. 16.
    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(3), 459–467 (2005)CrossRefGoogle 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

Personalised recommendations