A Change Detection Technique Using Rough C-Means on Medical Images

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)


Change Detection plays an important role in detecting various kinds of dissimilarities between two images of the same object over a period of time. This has extensive use in medical imaging and remote sensing. We propose a method of change detection where we first apply a change vector analysis (CVA), then apply clustering on the image by Rough C-means (RCM) and finally threshold it to obtain the Difference image (DI). RCM provides the concepts of upper approximation n and lower approximation which lead to better clustering and decrease the error rate. Experimental results of the proposed method are compared with existing change detection algorithms and it has been found to perform evidently better than the others.


Rough set Image segmentation Change vector analysis Thresholding 


  1. 1.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, London (1981)CrossRefGoogle Scholar
  3. 3.
    Gustafson, D.E., Kessel, W.C.: Fuzzy clustering with a fuzzy covariance matrix. In: IEEE Conference on Decision and Control (1979)Google Scholar
  4. 4.
    Dutta, S., Chattopadhyay, M.: A Study of Change Detection Algorithm for Medical Cell Images, IJITKM (2011)Google Scholar
  5. 5.
    Huang, L.K., Wang, M.J.: Image thresholding by minimizing the measures of fuzziness. Pattern Recogn. 28(1), 41–51 (1995)CrossRefGoogle Scholar
  6. 6.
    Kapur, J., Sahoo, P.K., Wong, K.C.: A new method for grey-level picture thresholding using the entropy of the histogram. CVGIP 29(3), 273–285 (1985)Google Scholar
  7. 7.
    Ridler, T., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybernet. 8, 630–632 (1978)CrossRefGoogle Scholar
  8. 8.
    Tsai, W.: Moment-preserving thresholding. Comput. Vision Graph. Image Process. 29, 377–393 (1985)CrossRefGoogle Scholar
  9. 9.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Systems Man Cybernet. 9, 62–66 (1979)CrossRefGoogle Scholar
  10. 10.
    Rosin, P.: Unimodal thresholding. Pattern Recogn. 34, 2083–2096 (2001)CrossRefGoogle Scholar
  11. 11.
    Skifstad, K., Jain, R.: Illumination independent change detection for real world image sequences. CVIP 46, 387–399 (1989)Google Scholar
  12. 12.
    Durcan, E., Ebrahimi, T.: Improved Linear Dependence and Vector Model for Illumination Invariant Change Detection. SPIE, Bellingham (2001)CrossRefGoogle Scholar
  13. 13.
    Aach, T.: Statistical model-based change detection in moving video. Signal Process. 31, 165–180 (1993)CrossRefGoogle Scholar
  14. 14.
    Tewkesbury, A.P., Comber, A.J., Tate, N.J., Lamb, A., Fisher, P.F.: A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens. Environ. 160, 1–14 (2015)CrossRefGoogle Scholar
  15. 15.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, USA (1992)zbMATHGoogle Scholar
  16. 16.
    Lingras, P., Georg, P.: Applying Rough Set Concepts to Clustering: Rough Sets: Selected Methods and Applications in Management and Engineering. Springer, London (2012)Google Scholar
  17. 17.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Education, London (2002)Google Scholar
  18. 18.
    Sehairi, K., Chouireb, F., Meunier, J.: Comparison study between different automatic threshold algorithms for motion detection. In: 4th International Conference on ICEE (2015)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSESTCETKolkataIndia
  2. 2.Department of CSEIIESTHowrahIndia

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