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Medical Image Segmentation Based on Beta Mixture Distribution for Effective Identification of Lesions

  • S. AnuradhaEmail author
  • C. H. Satyanarayana
Conference paper
  • 291 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 555)

Abstract

Brain imaging plays a vital role toward the identification of diseases such as seizures, lesions, sclerosis, and the other inhomogeneities. Methodologies for effective and efficient regulation of these diseases are to be planned so as to overcome the issues of mortality. This chapter highlights the contributions using beta mixture models in this direction. The experimentation is carried out in a MATLAB environment and the results are tabulated based on BRAINWEB images. The results are also compared with those of the existing models based on GMM using the performance evaluation parameters such as average difference, maximum distance, and image fidelity. The results showcase that the proposed methodology overcomes the GMM in all respects and it gives good recognization accuracy. The developed model can also be used for identifying the other diseases of the brain.

Keywords

Beta mixture model Mortality Sclerosis Inhomogeneity Lesion Mixture model 

References

  1. 1.
    Miller DH, Albert PS, Barkhof F, et al.: Guidelines for the use of magnetic resonance techniques in monitoring the treatment of multiple sclerosis. Ann Neurol; 39:6–16 (1996).Google Scholar
  2. 2.
    Paresh Chandra Barman et al.: MRI Image segmentation using level set method and implement an medical diagnosis system Computer Science & Engineering: An International Journal (CSEIJ), Vol. 1, No. 5, December 2011, pages 1–10, (2011).Google Scholar
  3. 3.
    S.Abdalla, N. Al-Aama Maryam, A.Al-Ghamdi: Development of MRI Brain Image Segmentation technique with Pixel Connectivity, International Journal of Scientific and Research Publications, Volume 6, Issue-6 (2016).Google Scholar
  4. 4.
    Ibtihal D. Mustafa, Mawai A. Hassan: A Comparison between Different Segmentation Techniques used in Medical Imaging, American Journal of Biomedical Engineering, 6(2) 59–69 (2016).Google Scholar
  5. 5.
    Ch Murali Krishna, Y. Srinivas: Unsupervised Image Segmentation Using Truncated Log Normal Distribution, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 1, (2015).Google Scholar
  6. 6.
    T.V. Madhusudhana Rao, S. Pallam Setty, Y. Srinivas: Content Based Image Retrievals for Brain Related Disease, International Journal of Computer Applications, Volume 85, No, 11, pp. 0975–8887 (2014).Google Scholar
  7. 7.
    Anamika Ahirwar: Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region classification of MR Brain Images, I.J. Information Technology and Computer Science, 05, 44–53, MECS (2013).Google Scholar
  8. 8.
    Q. Mahmood, A. Chodorowski, M. Persson: Automated MRI brain tissue segmentation based on mean shift and Fuzzy c-means using a priori tissue probability maps, IRBM 36, 185–196, Science Direct (2015).Google Scholar
  9. 9.
    Daniel Biediger, Christophe Collet and Jean-Paul Armspach: Multiple Sclerosis lesion detection with local multimodal Markovian analysis and cellular automata ‘growcut’’, Journal of Computational Surgery, Springer, 1:3, pp: 1–15.11 (2014).Google Scholar
  10. 10.
    F Rodrigo, M. Filipuzzi, R. Isoardi, M. Noceti, JP Graffigna: High intensity region segmentation in MR imaging of multiple sclerosis, Journal of Physics: Conference series 477, 012024, (2013).Google Scholar
  11. 11.
    B.R. Sajja, S. Datta, R. He, M. Mehta, R.K. Gupta, J.S. Wolinsky, P.A. Narayana: Unified approach for multiple sclerosis lesion segmentation on brain MRI, Ann. Biomed. Eng. 34 (1), 142–151, (2006).Google Scholar
  12. 12.
    Saurabh Shah, N.C. Chauhn: Classification of brain MRI Images using Computational Intelligent Techniques, International Journal of Computer Applications (0975-8887) Volume-124, No. 14, (2015).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of CSEGIT Gitam UniversityVisakhapatnamIndia
  2. 2.Department of CSEJNTUKakinadaIndia

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