An Image Segmentation Algorithm Using Iteratively the Mean Shift

  • Roberto Rodríguez
  • Ana G. Suarez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Image segmentation plays an important role in many systems of computer vision. The good performance of recognition algorithms depend on the quality of segmented image. According to the opinion of many authors the segmentation concludes when it satisfies the observer’s objectives, the more effective methods being the iterative. However, a problem of these algorithms is the stopping criterion. In this work the entropy is used as stopping criterion in the segmentation process by using recursively the mean shift filtering. In such sense a new algorithm is introduced. The good performance of this algorithm is illustrated with extensive experimental results. The obtained results demonstrated that this algorithm is a straightforward extension of the filtering process. In this paper a comparison was carried out between the obtained results with our algorithm and with the EDISON System [16].


Entropy image segmentation mean shift smoothing filter 


  1. 1.
    Kenong, W., Gauthier, D., Levine, M.D.: Live Cell Image Segmentation. IEEE Transactions on Biomedical Engineering 42(1) (June 1995)Google Scholar
  2. 2.
    Sijbers, J., Scheunders, P., Verhoye, M., Van der Linden, A., Van Dyck, D., Raman, E.: Watershed-based segmentation of 3D MR data for volume quatization. Magnetic Resonance Imaging 15(6), 679–688 (1997)CrossRefGoogle Scholar
  3. 3.
    Chin-Hsing, C., Lee, J., Wang, J., Mao, C.W.: Color image segmentation for bladder cancer diagnosis. Mathl. Comput. Modeling 27(2), 103–120 (1998)CrossRefzbMATHGoogle Scholar
  4. 4.
    Rodríguez, R., Alarcón, T., Wong, R., Sanchez, L.: Color segmentation applied to study of the angiogenesis. Part I. Journal of Intelligent and Robotic System 34(1) (May 2002)Google Scholar
  5. 5.
    Schmid, P.: Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Trans. Med. Imag. 18(2) (February 1999)Google Scholar
  6. 6.
    Koss, J.E., Newman, F.D., Johnson, T.K., Kirch, D.L.: Abdominal organ segmentation using texture transforms and a hopfield neural network. IEEE Trans. Med. Imag. 18(7) (July 1999)Google Scholar
  7. 7.
    Braiek, E.B., Cheriet, M., Dore, V.: SKCS-A Separable Kernel Family with Compact Support to improve visual segmentation of handwritten data. Electronic Letters on Computer Vision and Image Anal. 5(1), 14–29 (2005)Google Scholar
  8. 8.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(5) (May 2002)Google Scholar
  9. 9.
    Chenyang, X., Dzung, P., Jerry, P.: Image Segmentation Using Deformable Models. In: Fitzpatrick, J.M., Sonka, M. (eds.) SPIE Handbook on Medical Imaging, Medical Image Analysis, May 2000, vol. III, pp. 129–174 (2000)Google Scholar
  10. 10.
    Vicent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transact. Pattern Anal. Machine Intell. 13, 583–593 (1991)CrossRefGoogle Scholar
  11. 11.
    Cheriet, M., Said, J.N., Suen, C.Y.: A Recursive Thresholding Technique for Image Segmentation. IEEE Transactions on Image Processing 7(6) (June 1998)Google Scholar
  12. 12.
    Comaniciu, D.I.: Nonparametric Robust Method for Computer Vision., Ph.D. thesis, New Brunswick, Rutgers, The State University of New Jersey (January 2000)Google Scholar
  13. 13.
    Fukunaga, K., Hostetler, L.D.: The Estimation of the Gradient of a Density Function. IEEE Trans. Information Theory 21, 32–40 (1975)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)CrossRefGoogle Scholar
  15. 15.
    Zhang, H., Fritts, J.E., Goldman, S.A.: A Entropy-based Objective Evaluation Method for Image Segmentation. In: Yeung, M.M., Lienhart, R.W., Li, C.-S. (eds.) Storage and Retrieval Methods and Applications for Multimedia 2004. Proceeding of The SPIE, vol. 5307, pp. 38–49 (2003)Google Scholar
  16. 16.
    EDISON: Robust Image Understanding Laboratory, Rutgers University,
  17. 17.
    Awate, S.P., Whitaker, R.T.: Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 28(3), 364–376 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roberto Rodríguez
    • 1
  • Ana G. Suarez
    • 1
  1. 1.Mathematics and Physics (ICIMAF), Digital Signal Processing GroupInstitute of CyberneticsLa HabanaCuba

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