Skip to main content
SpringerLink
Log in
Book cover

Mixture Models and Applications pp 325–348Cite as

Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation

  • Chapter
  • First Online:

  • 1424 Accesses

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

We propose in this work to improve the tasks of image segmentation and modeling through an unsupervised flexible learning approach. Our focus here is to develop an alternative mixture model based on a bounded generalized Gaussian distribution, which is less sensitive to over-segmentation and offers more flexibility in data modeling than the Gaussian distribution which is certainly not the best approximation for image segmentation. A maximum likelihood- (ML) based algorithm is applied for estimating the resulted model parameters. We investigate here the integration of both a spatial information (a prior information between neighboring pixels) and a minimum description length (MDL) principle into the model learning step in order to deal with the major problems of finding the optimal number of classes and also selecting the best model that describes accurately the dataset. Therefore, the proposed model has the advantage to maintain the balance between model complexity and goodness of fit. Obtained results on a large database of medical MR images confirm the effectiveness of the proposed approach and demonstrate its superior performance compared to some conventional methods.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.cma.mgh.harvard.edu/ibsr/.

  2. 2.

    http://sourceforge.net/projects/cardiac-mr/files.

References

  1. Abbasi, S., Tajeripour, F.: Detection of brain tumor in 3d MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219, 526–535 (2017)

    Article  Google Scholar 

  2. Agnello, L., Comelli, A., Ardizzone, E., Vitabile, S.: Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis. Int. J. Imaging Syst. Technol. 26(2), 136–150 (2016)

    Article  Google Scholar 

  3. Ahmadvand, A., Daliri, M.R.: Improving the runtime of MRF based method for MRI brain segmentation. Appl. Math. Comput. 256, 808–818 (2015)

    MathSciNet  MATH  Google Scholar 

  4. Al-Shaikhli, S.D.S., Yang, M.Y., Rosenhahn, B.: Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images. Comput. Med. Imaging Graph. 38(8), 725–734 (2014)

    Article  Google Scholar 

  5. Alhakami, W., ALharbi, A., Bourouis, S., Alroobaea, R., Bouguila, N.: Network anomaly intrusion detection using a nonparametric Bayesian approach and feature selection. IEEE Access 7, 52181–52190 (2019)

    Article  Google Scholar 

  6. Allili, M.S., Bouguila, N., Ziou, D.: Finite general Gaussian mixture modeling and application to image and video foreground segmentation. J. Electron. Imaging 17(1), 013005–013005 (2008)

    Article  Google Scholar 

  7. Asman, A.J., Landman, B.A.: Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 17(2), 194–208 (2013)

    Article  Google Scholar 

  8. Ayed, I.B., Li, S., Ross, I.: Embedding overlap priors in variational left ventricle tracking. IEEE Trans. Med. Imaging 28(12), 1902–1913 (2009)

    Article  Google Scholar 

  9. Bishop, C.M.: Pattern recognition. Mach. Learn. 128 (2006)

    Google Scholar 

  10. Bouguila, N.: Clustering of count data using generalized Dirichlet multinomial distributions. IEEE Trans. Knowl. Data Eng. 20(4), 462–474 (2008)

    Article  Google Scholar 

  11. Bouguila, N., ElGuebaly, W.: Integrating spatial and color information in images using a statistical framework. Expert Syst. Appl. 37(2), 1542–1549 (2010)

    Article  Google Scholar 

  12. Bouguila, N., Ziou, D.: On fitting finite Dirichlet mixture using ECM and MML. In: Pattern Recognition and Data Mining, Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 22–25, 2005, Proceedings, Part I. pp. 172–182 (2005)

    Chapter  MATH  Google Scholar 

  13. Bouguila, N., Ziou, D.: A probabilistic approach for shadows modeling and detection. In: IEEE International Conference on Image Processing 2005, vol. 1, pp. I–329. IEEE, Piscataway (2005)

    Google Scholar 

  14. Bourouis, S., Hamrouni, K.: 3D segmentation of MRI brain using level set and unsupervised classification. Int. J. Image Graph. 10(1), 135–154 (2010)

    Article  MathSciNet  Google Scholar 

  15. Bourouis, S., Hamrouni, K., Betrouni, N.: Automatic MRI brain segmentation with combined atlas-based classification and level-set approach. In: Image Analysis and Recognition, 5th International Conference, ICIAR. pp. 770–778 (2008)

    Google Scholar 

  16. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002)

    Article  Google Scholar 

  17. Channoufi, I., Bourouis, S., Bouguila, N., Hamrouni, K.: Color image segmentation with bounded generalized Gaussian mixture model and feature selection. In: 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018, Sousse, Tunisia, March 21–24, 2018. pp. 1–6 (2018)

    Google Scholar 

  18. Channoufi, I., Bourouis, S., Bouguila, N., Hamrouni, K.: Image and video denoising by combining unsupervised bounded generalized Gaussian mixture modeling and spatial information. Multimed. Tools Appl. 77(19), 25591–25606 (2018)

    Article  Google Scholar 

  19. Channoufi, I., Bourouis, S., Bouguila, N., Hamrouni, K.: Spatially constrained mixture model with feature selection for image and video segmentation. In: Image and Signal Processing-8th International Conference, ICISP 2018, Cherbourg, France, July 2–4, 2018, Proceedings. pp. 36–44 (2018)

    Google Scholar 

  20. Choy, S.K., Tong, C.S.: Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval. IEEE Trans. Image Process. 19(2), 281–289 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  21. Cocosco, C., Niessen, W., Netsch, T., Vonken, E., Lund, G., Stork, A., Viergever, M.: Automatic image-driven segmentation of the ventricles in cardiac cine MRI. J. Magn. Reson. Imaging 25(2), 366–374 (2008)

    Article  Google Scholar 

  22. Cohen, F.S., Cooper, D.B.: Simple parallel hierarchical and relaxation algorithms for segmenting noncausal Markovian random fields. IEEE Trans. Pattern Anal. Mach. Intell. 2, 195–219 (1987)

    Article  Google Scholar 

  23. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B Methodol. 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  24. Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. Image Process. 11(2), 146–158 (2002)

    Article  MathSciNet  Google Scholar 

  25. Fan, W., Sallay, H., Bouguila, N., Bourouis, S.: A hierarchical Dirichlet process mixture of generalized Dirichlet distributions for feature selection. Comput. Electr. Eng. 43, 48–65 (2015)

    Article  Google Scholar 

  26. Fradkin, M., Ciofolo, C., Mory, B., Hautvast, G., Breeuwer, M.: Comprehensive segmentation of cine cardiac MR images. Med. Image Comput. Comput. Assist. Interv. 11, 178–185 (2008)

    Google Scholar 

  27. Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet another survey on image segmentation: region and boundary information integration. Comput. Vis. ECCV 2002, 21–25 (2002)

    MATH  Google Scholar 

  28. Gao, G., Wen, C., Wang, H.: Fast and robust image segmentation with active contours and student’s-t mixture model. Pattern Recogn. 63, 71–86 (2017)

    Article  Google Scholar 

  29. Gass, T., Székely, G., Goksel, O.: Simultaneous segmentation and multiresolution nonrigid atlas registration. IEEE Trans. Image Process. 23(7), 2931–2943 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hung, W.L., Yang, M.S., Chen, D.H.: Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation. Pattern Recogn. Lett. 29(9), 1317–1325 (2008)

    Article  Google Scholar 

  31. Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: A survey. Med. Image Anal. 24(1), 205–219 (2015)

    Article  Google Scholar 

  32. Ilunga-Mbuyamba, E., Avina-Cervantes, J.G., Garcia-Perez, A., de Jesus Romero-Troncoso, R., Aguirre-Ramos, H., Cruz-Aceves, I., Chalopin, C.: Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing 220, 84–97 (2017)

    Article  Google Scholar 

  33. Ivanovska, T., Laqua, R., Wang, L., Schenk, A., Yoon, J.H., Hegenscheid, K., Völzke, H., Liebscher, V.: An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images. Comput. Med. Imaging Graph. 48, 9–20 (2016)

    Article  Google Scholar 

  34. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)

    Article  Google Scholar 

  35. Kelly, P.A., Derin, H., Hartt, K.D.: Adaptive segmentation of speckled images using a hierarchical random field model. IEEE Trans. Acoust. Speech Signal Process. 36(10), 1628–1641 (1988)

    Article  MATH  Google Scholar 

  36. Law, M.H., Figueiredo, M.A., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1154–1166 (2004)

    Article  Google Scholar 

  37. Lin, X., Cowan, B., Young, A.: Model-based graph cut method for segmentation of the left ventricle. Conf. Proc. IEEE Eng. Med. Biol. Socpp. 3, 3059–3062 (2005)

    Google Scholar 

  38. Lindblom, J., Samuelsson, J.: Bounded support gaussian mixture modeling of speech spectra. IEEE Trans. Speech and Audio Process. 11(1), 88–99 (2003)

    Article  Google Scholar 

  39. Liu, G., Wu, J., Zhou, S.: Probabilistic classifiers with a generalized Gaussian scale mixture prior. Pattern Recogn. 46(1), 332–345 (2013)

    Article  MATH  Google Scholar 

  40. Lorenzo-Valdés, M., Sanchez-Ortiz, G.I., Elkington, A., Mohiaddin, R., Rueckert, D.: Segmentation of 4d cardiac MR images using a probabilistic atlas and the EM algorithm. Med. Image Anal. 8(3), 255–265 (2004)

    Article  Google Scholar 

  41. Lynch, M., Ghita, O., Whelan, P.F.: Segmentation of the left ventricle of the heart in 3-d+t MRI data using an optimized nonrigid temporal model. IEEE Trans. Med. Imaging 27(2), 195–203 (2008)

    Article  Google Scholar 

  42. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2. pp. 416–423, IEEE, Piscataway (2001)

    Google Scholar 

  43. McLachlan, G., Peel, D.: Finite Mixture Models. John, New York (2004)

    MATH  Google Scholar 

  44. Menze, B.H., Leemput, K.V., Lashkari, D., Riklin-Raviv, T., Geremia, E., Alberts, E., Gruber, P., Wegener, S., Weber, M.A., Székely, G., Ayache, N., Golland, P.: A generative probabilistic model and discriminative extensions for brain lesion segmentation with application to tumor and stroke. IEEE Trans. Med. Imaging 35(4), 933–946 (2016)

    Article  Google Scholar 

  45. Najar, F., Bourouis, S., Bouguila, N., Belghith, S.: A comparison between different Gaussian-based mixture models. In: 14th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2017, Hammamet, Tunisia, October 30–Nov. 3, 2017. pp. 704–708 (2017)

    Google Scholar 

  46. Najar, F., Bourouis, S., Bouguila, N., Belghith, S.: A fixed-point estimation algorithm for learning the multivariate GGMM: application to human action recognition. In: 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). pp. 1–4 (2018)

    Google Scholar 

  47. Najar, F., Bourouis, S., Zaguia, A., Bouguila, N., Belghith, S.: Unsupervised human action categorization using a Riemannian averaged fixed-point learning of multivariate GGMM. In: Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition of Image Analysis and Recognition-15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018. pp. 408–415 (2018)

    Google Scholar 

  48. Najar, F., Bourouis, S., Bouguila, N., Belghith, S.: Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition. Multimed. Tools Appl. 1–23 (2019)

    Google Scholar 

  49. Nguyen, T.M., Wu, Q.J., Zhang, H.: Bounded generalized gaussian mixture model. Pattern Recogn. 47(9), 3132–3142 (2014)

    Article  MATH  Google Scholar 

  50. Ozden, M., Polat, E.: A color image segmentation approach for content-based image retrieval. Pattern Recogn. 40(4), 1318–1325 (2007)

    Article  MATH  Google Scholar 

  51. Paragios, N.: A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans. Med. Imaging 22(6), 773–776 (2003)

    Article  Google Scholar 

  52. Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A., Wright, G.: Evaluation framework for algorithms segmenting short axis cardiac MRI. In: The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge (2009)

    Google Scholar 

  53. Rajalakshmi, N., Lakshmi Prabha, V.: Mri brain image classification—a hybrid approach. Int. J. Imaging Syst. Technol. 25(3), 226–244 (2015)

    Article  Google Scholar 

  54. Rezaee, M.R., van der Zwet, P.M.J., Lelieveldt, B.P.E., van der Geest, R.J., Reiber, J.H.C.: A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Trans. Image Process. 9(7), 1238–1248 (2000)

    Article  Google Scholar 

  55. Ribbens, A., Hermans, J., Maes, F., Vandermeulen, D., Suetens, P.: Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images. IEEE Trans. Med. Imaging 33(2), 201–224 (2014)

    Article  Google Scholar 

  56. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  57. Roth, V., Ommer, B.: Exploiting low-level image segmentation for object recognition. In: Joint Pattern Recognition Symposium. pp. 11–20, Springer, Berlin (2006)

    Google Scholar 

  58. Saritha, S., Amutha Prabha, N.: A comprehensive review: Segmentation of MRI images—brain tumor. Int. J. Imaging Syst. Technol. 26(4), 295–304 (2016)

    Article  Google Scholar 

  59. Sefidpour, A., Bouguila, N.: Spatial color image segmentation based on finite non-Gaussian mixture models. Expert Syst. Appl. 39(10), 8993–9001 (2012)

    Article  Google Scholar 

  60. Tenenbaum, J.M., Barrow, H.G.: Experiments in interpretation-guided segmentation. Artif. Intell. 8(3), 241–274 (1977)

    Article  Google Scholar 

  61. Thapaliya, K., Pyun, J.Y., Park, C.S., Kwon, G.R.: Level set method with automatic selective local statistics for brain tumor segmentation in MR images. Comput. Med. Imaging Graph. 37(7–8), 522–537 (2013)

    Article  Google Scholar 

  62. Uzümcü, M., van der Geest, R., Swingen, C., Reiber, J., Lelieveldt, B.: Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming. Invest. Radiol. 41(1), 52–62 (2006)

    Article  Google Scholar 

  63. van der Lijn, F., de Bruijne, M., Klein, S., den Heijer, T., Hoogendam, Y.Y., van der Lugt, A., Breteler, M.M.B., Niessen, W.J.: Automated brain structure segmentation based on atlas registration and appearance models. IEEE Trans. Med. Imaging 31(2), 276–286 (2012)

    Article  Google Scholar 

  64. Verma, H., Agrawal, R.K., Kumar, N.: Improved fuzzy entropy clustering algorithm for MRI brain image segmentation. Int. J. Imaging Syst. Technol. 24(4), 277–283 (2014)

    Article  Google Scholar 

  65. Wang, L., Pan, C.: Image-guided regularization level set evolution for MR image segmentation and bias field correction. Magn. Reson. Imaging 32(1), 71–83 (2014)

    Article  Google Scholar 

  66. Xia, Y., Ji, Z., Zhang, Y.: Brain MRI image segmentation based on learning local variational Gaussian mixture models. Neurocomputing 204, 189–197 (2016), big Learning in Social Media Analytics Containing a selection of papers from the 2014 International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC2014)

    Google Scholar 

  67. Yang, X., Krishnan, S.M.: Image segmentation using finite mixtures and spatial information. Image Vis. Comput. 22(9), 735–745 (2004)

    Article  Google Scholar 

  68. Yang, M.S., Lai, C.Y., Lin, C.Y.: A robust EM clustering algorithm for gaussian mixture models. Pattern Recogn. 45(11), 3950–3961 (2012)

    Article  MATH  Google Scholar 

  69. Yousefi, S., Kehtarnavaz, N., Gholipour, A.: Improved labeling of subcortical brain structures in atlas-based segmentation of magnetic resonance images. IEEE Trans. Biomed. Eng. 59(7), 1808–1817 (2012)

    Article  Google Scholar 

  70. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)

    Article  Google Scholar 

  71. Zhang, Z., Chen, C., Sun, J., Chan, K.L.: EM algorithms for gaussian mixtures with split-and-merge operation. Pattern Recogn. 36(9), 1973–1983 (2003)

    Article  MATH  Google Scholar 

  72. Zhou, S., Wang, J., Zhang, M., Cai, Q., Gong, Y.: Correntropy-based level set method for medical image segmentation and bias correction. Neurocomputing 234, 216–229 (2017)

    Article  Google Scholar 

  73. Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)

    Article  Google Scholar 

  74. Ziou, D., Bouguila, N.: Unsupervised learning of a finite gamma mixture using MML: application to SAR image analysis. In: ICPR (2). pp. 68–71 (2004)

    Google Scholar 

Download references

Acknowledgements

This research is based on a grant received from the research council (TCR)-Oman.

Author information

Authors and Affiliations

  1. Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, LR-SITI Laboratoire Signal, Image et Technologies de l’Information, Tunis, Tunisia

    Ines Channoufi

  2. Laboratoire RISC Robotique Informatique et Systémes Complexes, Université de Tunis El Manar, ENIT, Tunis, Tunisia

    Fatma Najar

  3. Taif University, Taif, Saudi Arabia

    Sami Bourouis

  4. Université de Tunis El Manar, LR-SITI Laboratoire Signal, Image et Technologies de l’Information, Tunis, Tunisia

    Sami Bourouis

  5. Department of Electrical and Computer Engineering (ECE), Concordia University, Montreal, QC, Canada

    Muhammad Azam

  6. Gulf College, Al Maabelah, Muscat, Oman

    Alrence S. Halibas

  7. Taif University, Taif, Saudi Arabia

    Roobaea Alroobaea

Authors
  1. Ines Channoufi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatma Najar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sami Bourouis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Azam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alrence S. Halibas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roobaea Alroobaea
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ali Al-Badi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sami Bourouis .

Editor information

Editors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

  2. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Channoufi, I. et al. (2020). Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation. In: Bouguila, N., Fan, W. (eds) Mixture Models and Applications. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-23876-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23876-6_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23875-9

  • Online ISBN: 978-3-030-23876-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation