Fast Adapting Mixture Parameters Schemes for Probability Density Difference-Based Deformable Model

  • Aicha Baya GoumeidaneEmail author
  • Nafaa Nacereddine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


This paper presents a new region-driven active contour using the pdf difference to evolve. The pdf estimation is done via a new and fast Gaussian mixture model (GMM) parameters updating scheme. The experiments performed on synthetic and X-ray images have shown not only an accurate contour delineation but also outstanding performance in terms of execution speed compared to the GMM estimation based on EM algorithm and to non-parametric pdf estimations.


Active contour Adaptive mixture GMM parameters updates 


  1. 1.
    Abd-Almageed, W., Ramadan, S., Smith, C.: Kernel snakes: non-parametric active contour models. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 1131–1147 (2003)Google Scholar
  2. 2.
    Akram, F., Jeong, H.K., Lim, H.U., Nam, C.K.: Segmentation of intensity inhomogeneous brain MR images using active contours. Comput. Math. Methods Med. 2014, 1–14 (2014)CrossRefGoogle Scholar
  3. 3.
    Cohen, L., Cohen, I.: Finite-element methods for active contour models and balloons for 2D and 3D images. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1131–1147 (1993)CrossRefGoogle Scholar
  4. 4.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Gao, G., Wen, C., Wang, H.: Fast and robust image segmentation with active contours and student’s-t mixture model. Pattern Recogn. 3(C), 71–86 (2017)CrossRefGoogle Scholar
  6. 6.
    Goumeidane, A.B., Khamadja, M., Odet, C.: Parametric active contour for boundary estimation of weld defects in radiographic testing. In: Proceedings of ISSPA, pp. 1–4, September 2007Google Scholar
  7. 7.
    Goumeidane, A.B., Nacereddine, N.: Spatially varying weighting function-based global and local statistical active contours. application to x-ray images. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 181–192. Springer, Cham (2016). Scholar
  8. 8.
    Goumeidane, A., Nacereddine, N., Kahamdja, M.: Computer aided weld defect delination using active contours in radiographic inspection. J. X-Ray Sci. Technol. 23(3), 289–310 (2015)CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, A.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  10. 10.
    Li, B., Acton, S.T.: Automatic active model initialization via Poisson inverse gradient. IEEE Trans. Image Process. 17(8), 1406–1420 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Nacereddine, N., Hamami, L., Ziou, D., Goumeidane, A.B.: Adaptive B-spline model based probabilistic active contour for weld defect detection in radiographic imaging. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 2. AISC, vol. 84, pp. 289–297. Springer, Heidelberg (2010). Scholar
  12. 12.
    Nishio, M., Tanaka, Y.: Heterogeneity in pulmonary emphysema: analysis of ct attenuation using Gaussian mixture model. PLoS ONE 13(2), e0192892 (2018)CrossRefGoogle Scholar
  13. 13.
    Parzen, E.: On the estimation of a probability density function and the mode. Ann. Math. Stat. 33, 1065–1076 (1962)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Priebe, C.: Adaptive mixturesa. J. Am. Stat. Assoc. 89, 796–806 (1994)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Scott, D.: Averaged shifted histogram: effective non parametric density estimators in several dimensions. Ann. Stat. 13(3), 1024–1040 (1985)CrossRefGoogle Scholar
  16. 16.
    Titterington, D.M.: Recursive parameter estimation using incomplete data. J. R. Stat. Soc. Ser. B 46, 257–267 (1984)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Yin, S., Zhang, Y., Karim, S.: Large scale remote sensing image segmentation based on fuzzy region competition and Gaussian mixture model. IEEE Access 6 (2018)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Research Center in Industrial Technologies CRTIAlgiersAlgeria

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