Bi-dimensional Statistical Empirical Mode Decomposition-Based Video Analysis for Detecting Colon Polyps Using Composite Similarity Measure

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 309)


The third leading cause of all deaths from cancer is colorectal cancer (10 % of the total for men and 9.2 % of the total in case of women) (Globocan in Cancer Incidence and Morality Worldwide (2008) [1]. The only prevention is to detect and remove the cancerous adenomatous polyps during optical colonoscopy (OC). This paper proposes bi-dimensional statistical empirical mode decomposition (BSEMD)-based colon polyp detection strategy, wherein a composite similarity measure (CSM) has been used. In this work, separate sets of training and testing samples are opted. Only few samples are randomly chosen for training database, remaining samples make the testing database. The proposed method is implemented on sequences of sample images from an OC video database (Park et al in IEEE Trans. Biomed. Eng 59:1408–1418) [2] provided by American College of Gastroenterology (American College of Gastroenterology, [3]. PCA-based feature extraction is used in this work, as it reduces the dimensions efficiently from the main object. The obtained results demonstrate the achieved improvement in the recognition rates, in comparison with other detection procedures.


Bi-dimensional statistical empirical mode decomposition (BSEMD) Composite similarity measure (CSM) Colon polyps (CP) Optical colonoscopy (OC) 



The work is supported by University Grants Commission, India (UGC) under the University with Potential for Excellence (UPE), phase II scheme awarded to Jadavpur University, Kolkata, India.


  1. 1.
    Globocan: Cancer Incidence and Morality Worldwide, (2008)
  2. 2.
    Park, S.Y., Sargent, D., Spofford, I., Vosburgh, K.G., Rahim, Y.A.: A colon video analysis framework for polyp detection. IEEE Trans. Biomed. Eng. 59(5), 1408–1418 (2012)CrossRefGoogle Scholar
  3. 3.
    American College of Gastroenterology,
  4. 4.
    Ganz, M., Yang, X., Slabaugh, G.: Automatic segmentation of polyps in colonoscopic narrow-band imaging data. IEEE Trans. Biomed. Eng. 59(8), 2144–2151 (2012)CrossRefGoogle Scholar
  5. 5.
    Medical Supplies & Equipment Co.,
  6. 6.
    Tian, Y., Zhao, K., Yiping, X., Peng, F.: An image compression method based on the multi-resolution characteristics of BEMD. Comput. Math. Appl. 61(8):2142–2147, Springer (2011)CrossRefGoogle Scholar
  7. 7.
    Arfia, F.B.,Sabri, A., Messaoud, M.B., Abid, M.: The bidimensional empirical mode decomposition with 2D-DWT for gaussian image denoising. In: Proceedings of 17th International Conference on Digital Signal Processing, pp. 1–5. Corfu (2011)Google Scholar
  8. 8.
    Pan, J., Zhang, D., Tang, Y.: A Fractal-based BEMD method for image texture analysis. In: IEEE International Conference on Systems Man and Cybernetics (SMC), pp. 3817–3820. Istanbul (2010)Google Scholar
  9. 9.
    Zhang, B., Zhang, C., Wu, J., Liu, H.: A medical image fusion method based on energy classification of BEMD components. Optik 125(1), 146–153 (2014)CrossRefGoogle Scholar
  10. 10.
    Tran, V.T., Yang, B.S., Gu, F., Ball, A.: Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagosis. Mech. Syst. Signal Process. 38(2), 601–614 (2013). SpringerCrossRefGoogle Scholar
  11. 11.
    Chen, W.K., Lee, J.C., Han, W.Y., Shis, C.K., Chang, K.C.: Iris recognition based on bi-dimensional empirical mode decomposition and fractal dimension. Inf. Sci. 221, 439–451 (2013). SpringerCrossRefGoogle Scholar
  12. 12.
    Kim, D., Park, M., Oh, H.S.: Bidimensional statistical empirical mode decomposition. IEEE Signal Process. Lett. 19(4), 191–194 (2012)CrossRefGoogle Scholar
  13. 13.
    Jolliffe I.T.: Principal component analysis. Series: Springer Series in Statistics, 2nd edn., pp. 487. Springer, New York (2002)Google Scholar
  14. 14.
    Goshtasby, A.A.: Similarity and dissimilarity measures. Image Registration Principles, Tools and Methods, pp. 7–66. Springer London (2012)Google Scholar
  15. 15.
    Maroulis, D.E., Iakovidis, D.K., Karkanis, S.A., Karras, D.A.: CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput. Methods Programs Biomed, vol. 70, (2), pp. 151–166. Springer-Verlog (2003)Google Scholar
  16. 16.
    Karkanis, S.A., Iakovidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inf. Tech. Biomed. 7(3), 141–152 (2003)CrossRefGoogle Scholar
  17. 17.
    Iakovidis, D.K., Maroulis, D.E., Karkanis, S.A.: An intelligent system for automatic detection of gastrointestinal edennomas in video endoscopy. Comput. Biol. Med. 36(10), 1084–11003 (2006). SpringerCrossRefGoogle Scholar
  18. 18.
    Huang, N.E., Wu, M.L., Qu, W., Long, S.R., Shen, S.S.: Applications of hilbert-huang transform to non-stationary financial time series analysis. Appl. Stochast. Mod. Bus. Ind. 19(3), 245–268 (2003)CrossRefMATHMathSciNetGoogle Scholar
  19. 19.
    Linderhed, A.: 2D empirical mode decompositions-in the spirit of image compression. In: Proceedings SPIE 4738, Wavelet and Independent Component Analysis applications IX, vol. 4738, pp. 1–8 (2002)Google Scholar
  20. 20.
    Ge, G., Yu, L.: Extrema points coding based on empirical mode decomposition: an improved image sub-band coding method. Comput. Elect. Eng. 39(3), 882–892 (2013)CrossRefGoogle Scholar
  21. 21.
    Spearman, C.: The proof and measurement of association between two things. J. Psychol. 15(1), 72–101 (1904)CrossRefGoogle Scholar
  22. 22.
    Pearson, K.: Contributions to the mathematical theory of evaluation, III, regression, heredity, and panmixia. Philos. Trans. R. Soc. Lond. Ser. A 187, 253–318 (1896)CrossRefMATHGoogle Scholar

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© Springer India 2015

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentJadavpur UniversityKolkataIndia

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