Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images

  • Tarek Elguebaly
  • Nizar Bouguila
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)

Abstract

In the context of biomedical image processing and bioinformatics, an important problem is the development of accurate models for image segmentation and DNA spot detection. In this paper we propose a highly efficient unsupervised Bayesian algorithm for biomedical image segmentation and spot detection of cDNA microarray images, based on generalized Gaussian mixture models. Our work is motivated by the fact that biomedical and cDNA microarray images both contain non-Gaussian characteristics, impossible to model using rigid distributions like the Gaussian. Generalized Gaussian mixture models are robust in the presence of noise and outliers and are more flexible to adapt the shape of data.

Keywords

Image Segmentation Biomedical Image Bayesian Learn Spot Detection Microarray Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Cho, S.-B., Won, H.-H.: Machine Learning in DNA Microarray Analysis for Cancer Classification. In: Proc. of the First Asia-Pacific Bioinformatics Conference, pp. 189–198 (2003)Google Scholar
  2. 2.
    Katzer, M., Kummert, F., Sagerer, G.: Methods for Automatic Microarray Image Segmentation. IEEE Transactions on NanoBioscience 2(4), 202–214 (2003)CrossRefGoogle Scholar
  3. 3.
    Pappas, T.N.: An Adaptive Clustering Algorithm for Image Segmentation. IEEE Transactions on Signal Processing 40(4), 901–914 (1992)CrossRefGoogle Scholar
  4. 4.
    Yonghong, H., Englehart, K.B., Hudgins, B., Chan, A.D.C.: A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses. IEEE Transactions on Biomedical Engineering 52(11), 1801–1811 (2005)CrossRefGoogle Scholar
  5. 5.
    Rocke, D.M., Durbin, B.: A Model for Measurement Error for Gene Expression Arrays. Journal of Computational Biology 8(6), 557–569 (2004)CrossRefGoogle Scholar
  6. 6.
    Bouguila, N., Ziou, D., Monga, E.: Practical Bayesian Estimation of a Finite Beta Mixture Through Gibbs Sampling and its Applications. Statistics and Computing 16(2), 215–225 (2006)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Gao, Z., Belzer, B., Villasenor, J.: A Comparison of the Z, E8, and Leech Lattices for Quantization of Low Shape-Parameter Generalized Gaussian Sources. IEEE Signal Processing Letters 2(10), 197–199 (1995)CrossRefGoogle Scholar
  8. 8.
    Meignen, S., Meignen, H.: On the Modeling of Small Sample Distributions with Generalized Gaussian Density in a Maximum Likelihood Framework. IEEE Transactions on Image Processing 15(6), 1647–1652 (2006)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Sharifi, K., Leon-Garcia, A.: Estimation of Shape Parameter for Generalized Gaussian Distributions in Subband Decomposition of Video. IEEE Transactions on Circuits and Systems for Video Technology 5(1), 52–56 (1995)CrossRefGoogle Scholar
  10. 10.
    Aiazzi, B., Alpaone, L., Baronti, S.: Estimation Based on Entropy Matching for Generalized Gaussian PDF Modeling. IEEE Signal Processing Letters 6(6), 138–140 (1999)CrossRefGoogle Scholar
  11. 11.
    Kokkinakis, K., Nandi, A.K.: Exponent Parameter Estimation for Generalized Gaussian Probability Density Functions with Application to Speech Modeling. Signal Processing 85(9), 1852–1858 (2005)MATHCrossRefGoogle Scholar
  12. 12.
    Varanasi, M.K., Aazhang, B.: Parametric Generalized Gaussian Density Estimation. The Journal of the Acoustical Society of America 86(4), 1404–1415 (1989)CrossRefGoogle Scholar
  13. 13.
    Pi, M.: Improve Maximum Likelihood Estimation for Subband GGD Parameters. Pattern Recognition Letters 27(14), 1710–1713 (2006)CrossRefGoogle Scholar
  14. 14.
    Allili, M.S., Bouguila, N., Ziou, D.: Finite General Gaussian Mixture Modeling and Application to Image and Video Foreground Segmentation. Journal of Electronic Imaging 17(1), 1–13 (2008)CrossRefGoogle Scholar
  15. 15.
    Fan, S.-K.S., Lin, Y.: A Fast Estimation Method for the Generalized Gaussian Mixture Distribution on Complex Images. Computer Vision and Image Understanding 113(7), 839–853 (2009)CrossRefGoogle Scholar
  16. 16.
    Robert, C.P.: The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation, 2nd edn. Springer, Heidelberg (2007)MATHGoogle Scholar
  17. 17.
    Robert, C.P., Casella, G.: Monte Carlo Statistical Methods, 2nd edn. Springer, Heidelberg (2004)MATHGoogle Scholar
  18. 18.
    Gentle, J.E., Härdle, W.: Handbook of Computational Statistics. In: Concepts and Fundamentals, vol. 1, Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Lewis, S.M., Raftery, A.E.: Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator. Journal of the American Statistical Association 90, 648–655 (1997)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Yu, J., Tan, J.: Object Density-Based Image Segmentation and its Applications in Biomedical Image Analysis. Computer Methods and Programs in Biomedicine 96(3), 193–204 (2009)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Larson, G.W., Rushmeier, H., Piatko, C.: A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes. IEEE Transactions on Visualization and Computer Graphics 3(4), 291–306 (1997)CrossRefGoogle Scholar
  22. 22.
    Wu, S., Yan, H.: Microarray Image Processing Based on Clustering and Morphological Analysis. In: Proc. of the First Asia Pacific Bioinformatics Conference, pp. 111–118 (2003)Google Scholar
  23. 23.
    Rueda, L., Qin, L.: An Improved Clustering-based Approach for DNA Microarray Image Segmentation. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 644–652. Springer, Heidelberg (2004)Google Scholar
  24. 24.
    Callow, M.J., Dudoit, S., Gong, E.L., Speed, T.P., Rubin, E.M.: Microarray Expression Profiling Identifies Genes with Altered Expression in HDL Deficient Mice. Genome Research 10(12), 2022–2029 (2000)CrossRefGoogle Scholar
  25. 25.
    Brown, P., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genetics, 33–37 (1999)Google Scholar
  26. 26.
    Qin, L., Rueda, L., Ali, A., Ngom, A.: Spot Detection and Image Segmentation in DNA Microarray Data. Applied Bioinformatics 4(1), 1–11 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tarek Elguebaly
    • 1
  • Nizar Bouguila
    • 1
  1. 1.CIISE, Faculty of Engineering and Computer ScienceConcordia UniversityMontrealCanada

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