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MRI-Based Medical Image Enhancement Technique Using Particle Swarm Optimization

  • S. Sakthivel
  • V. Prabhu
  • R. Punidha
Conference paper
  • 16 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 626)

Abstract

The recent work tends to a complexity improvement strategy, which joins the established difference upgrade approach. The primary targets of this paper are to expand the data substance and upgrade the subtleties of a picture utilizing the examination procedure of parameters bolstered by Particle Swarm Optimization (PSO) calculation. Here, PSO from swarm intellect (SI) has applied to appraise the consideration values. In the proposed technique, the edge closeness of data parameters, for example, mean, standard deviation, and difference have utilized to detail the improvement strategy. These strategies defeat the past Level-3 disintegration to extricate highlights from pictures of PSO methods. A reproduction result is a proposed particle swarm optimization based contrast enhance strategy that improves the general picture differentiate and enhances the data content in the picture. Additionally, constraints of Peak Signal-to-Noise Ratio (PS-to-NR) and Mean Squared Error (MSE) have investigated the Particle Swarm Optimization (PSO) image in Fig. 1. We contrast and other difference upgrade procedures, the proposed technique gives hidden information of a picture and it is progressively reasonable for applications in early tumor location.
Fig. 1

PSO image that represents the particle orientation

Keywords

MRI images PSO technique Edge similarity index Parameters 

Notes

Acknowledgements

One of the authors would like to notify that there is no conflict on brain MRI image dataset in this paper and authors to thank the reviewer for their valuable suggestions.

References

  1. 1.
    A. Gorajand, A. Ghosh, Grey-level Image Enhancement By Particle Swarm Optimization. World Congress Nat. Biologically Inspired Comput. (NaBIC) 978-1-4244-5612-3/09/$26.00 @ 2009 IEEEGoogle Scholar
  2. 2.
    V. Selvi et al., Comparative analysis of ant colony and particle swarm optimization techniques. Int. J. Comput. Appl. 5(4), 1–6 (2010)Google Scholar
  3. 3.
    A. Sharma et al., Recent trends and techniques in image segmentation using Particle Swarm Optimization-a survey. J. Sci. Res. Public. 5(6), 6 (2015)Google Scholar
  4. 4.
    A.M. Nickfarjam et al., Multi resolution gray level image enhancement using particle swarm optimization. Springer 47(4), 1132–1143 (2017)Google Scholar
  5. 5.
    G. Qinqing, C. Dexin, Z. Guangping, H. Ketai, Image enhancement technique based on improved PSO algorithm, in 2011 6th IEEE Conference on Industrial Electronics and Applications (Beijing, 2011, pp. 234-238).  https://doi.org/10.1109/ICIEA.2011.5975586
  6. 6.
    G. Mohan, M. Subashini, MRI based medical image analysis: Survey on brain tumor grade classification. Biomed. Signal Process. Control. 39, 139–161 (2018).  https://doi.org/10.1016/j.bspc.2017.07.007
  7. 7.
    J. Kennedy, R. Eberhart, Particle swarm optimization. IEEE (1995), 0-7803-2768-3/95/$4.00 0 @ 1995Google Scholar
  8. 8.
    L.L.Z. Haiyin, A method of image enhancement based on genetic algorithm. Math. Theory Appl., Category Index:TN911.73Google Scholar
  9. 9.
    R. Punidha, S. Sakthivel et al., Segmentation of brain tumor and its area calculation in brain MR images using K-Mean clustering and fuzzy C-Mean algorithm. Indian J. Pure Math. 116(23)Google Scholar
  10. 10.
    R.S. Kabade, M.S. Gaikwad, Segmentation of brain tumor &its area calculation in brain MR images using K-Mean clustering and fuzzy C-Mean algorithm. Int. J. Comput. Sci. Eng. Technol. 4(05) (2013)Google Scholar
  11. 11.
    S. Bauer, R. Wiest, L.P. Nolte, M. Reyes, A survey of MRI based medical image analysis for brain tumor studies. Phys. Med. Biol. 58, R97–R129 (2013)CrossRefGoogle Scholar
  12. 12.
    H. Chen, J. Tian, Particle swarm optimization algorithm for image enhancement. Int. Conf. Uncertainty Reasoning Knowl. Eng. 1, 154–157 (2011)CrossRefGoogle Scholar
  13. 13.
    P. Mohan et al., Intelligent based brain tumor detection using ACO. Int. J. Innov. Res. Comput. Commun. Eng. 1(9), 2143–2150 (2013)Google Scholar
  14. 14.
    Suneetha et al., Enhancement techniques for gray scale images in spatial domain. Int. J. Emerg. Technol. Adv. Eng. 3(2), 13–20 (2012)Google Scholar
  15. 15.
    P.G. Kuppusamy et al., A full reference morphological edge similarity Index to account processing induced edge artefacts in magnetic resonance images. Sci. Direct 37(1), 159–166 (2017)Google Scholar
  16. 16.
    M. Kanmani, V. Narshimhan, An image contrast enhancement algorithm for gray-scale images using particle swarm optimization (2018)Google Scholar
  17. 17.
    M.G.H. Omran, Particle swarm optimization methods for pattern recognition and image processing. University of Pretoria etd (2005)Google Scholar
  18. 18.
    M. Bashir et al., Performance analysis of particle swarm optimization algorithm-based parameter tuning for fingerprint image enhancement. 1(7) (2016)Google Scholar
  19. 19.
    N. Singh et al., Parameter Optimization in Image Enhancement using PSO. Am. J. Eng. Res. 2(5), 84–90 (2013)Google Scholar
  20. 20.
    S. Dhariwal, Comparative analysis of various image enhancement techniques. Int. J. Electron. Commun. Technol. 2(3), 91–95 (2011)Google Scholar
  21. 21.
    M. Abdullah Al Wadud et al., A spatially controlled histogram equalizations for image enhancement. IEEE (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • S. Sakthivel
    • 1
    • 2
  • V. Prabhu
    • 3
  • R. Punidha
    • 4
  1. 1.Anna UniversityChennaiIndia
  2. 2.Department of CSEVel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, AvadiChennaiIndia
  3. 3.Department of ECEVel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, AvadiChennaiIndia
  4. 4.Department of CSEBharathiyar Institute of Engineering for WomanSalemIndia

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