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Particle Swarm Optimization Based Fast Fuzzy C-Means Clustering for Liver CT Segmentation

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Applications of Intelligent Optimization in Biology and Medicine

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 96))

Abstract

A Fast Fuzzy C-Means (FFCM) clustering algorithm, optimized by the Particle Swarm Optimization (PSO) method, referred to as PSOFFCM, has been introduced and applied on liver CT images. Compared to FFCM, the proposed approach leads to higher values in terms of Jaccard Index and Dice Coefficient, and thus, indicating higher similarity with the ground truth provided. Based on ANOVA analysis, PSOFFCM showed better results in terms of Dice Coefficient. It also showed better mean values in terms of Jaccard Index and Dice Coefficient based on the box and whisker plots.

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Notes

  1. 1.

    http://radiopaedia.org/search?q=CT&scope=all,18.3.20131:44PM.

References

  1. Anter, A., Azar, A., Hassanien, A., El-Bendary, N., ElSoud, M.: Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques. In: IEEE Proceedings of Federated Conference on Computer Science and Information Systems, pp. 193–198 (2013)

    Google Scholar 

  2. Albrecht, T., et al.: Guidelines for the use of contrast agents in ultrasound. Ultraschall Med. 25(4), 249–256 (2004)

    Article  Google Scholar 

  3. ANOVA. http://www.mathworks.com/help/stats/anova.html. Accessed 13 Dec 2013

  4. Ashman, J.: Measuring named entity similarity through wikipedia category hierarchies. M.Sc. thesis. The University of Texas at Arlington: USA (2010)

    Google Scholar 

  5. Annadurai, S., Shanmugalakshmi, R.: Fundamentals of Digital Image Processing. Pearson Education India (2006)

    Google Scholar 

  6. Bezdek, J.: Fuzzy Mathematics in Pattern Classification. Ph.D. thesis, Applied Mathematic Center, Cornell University, Ithaca, New York (1973)

    Google Scholar 

  7. Bluemke, D., Fishman, E.: Spiral CT of the liver. AIR 160, 787–792 (1993)

    Google Scholar 

  8. Babalola, K., Patenaude, B., Aljabar, P., Schnabel, J., Kennedy, D., Crum, W., Smith, S., Cootes, T.F., Jenkinson, M., Rueckert, D.: Comparison and evaluation of segmentation techniques for subcortical structures in Brain MRI. In: Medical Image Computing and Computer-Assisted Intervention MICCAI. Lecture Notes in Computer Science, vol. 5241, pp. 409–416 (2008)

    Google Scholar 

  9. Bronzino, J.: The Biomedical Engineering Handbook. 2. Springer (2000)

    Google Scholar 

  10. Bluemke, D., Urban, B., Fishman, E.: Spiral CT of the liver: current applications. Semin. Ultrasound CT MR 15, 107–121 (1994)

    Article  Google Scholar 

  11. Bush, B.: Fuzzy Clustering Techniques: Fuzzy C-Means and Fuzzy Min-Max Clustering Neural Networks. http://benjaminjamesbush.com/fuzzyclustering/fuzzyclustering.docx. Accessed 14 Dec 2013

  12. Couceiro, M.S., Martins, F.M., Rocha, R.P., Ferreira, N.M.: Analysis and parameter adjustment of the RDPSO—towards an understanding of Robotic network dynamic partitioning based on Darwin’s theory. Int. Math. Forum Hikari Ltd. 7(32), 1587–1601 (2012)

    MathSciNet  MATH  Google Scholar 

  13. Cao, B., Wang, G., Chen, S., Guo, S.: Fuzzy Information and Engineering 2010, vol 1. Springer (2010)

    Google Scholar 

  14. Digestive Disorders Health Center. http://www.webmd.com/digestive-disorders/picture-of-the-liver. Accessed 8 Dec 2013

  15. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn., chap. 2, pp. 20–63. Wiley, Chichester (2001)

    Google Scholar 

  16. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: IEEE Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88 (2000)

    Google Scholar 

  17. Frakes, W.B., Baeza-Yates, R.: Information Retrieval, Data Structure and Algorithms. Prentice Hall (1992)

    Google Scholar 

  18. Hartman, B.: Maths AQA. Letts and Lonsdale (2006)

    Google Scholar 

  19. Huang, X., Tsechpenakis, G.: Medical image segmentation. In: Hristidis, V. (ed.) Information Discovery on Electronic Health Records. Chap. 10, Chapman & Hall (2009)

    Google Scholar 

  20. Izakian, H., Abraham, A.: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst. Appl. 38(3), 1835–1838 (2011)

    Article  Google Scholar 

  21. Jaccard, P.: Etude Comparative de la distribution Orale Dansune Portion des Alpes et des Jura. Bulletin del la Socit Vaudoise des Science Naturelles, vol. 37, pp. 547–579 (1901)

    Google Scholar 

  22. Kennedy, J., Eberhart, R.: A new optimizer using particle swarm theory. In: Proceedings of the IEEE Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. Nagoya, Japan (1995)

    Google Scholar 

  23. Läthén, G.: Segmentation methods for digital image analysis: blood vessels, multi-scale filtering, and level set methods. Linköping studies in science and technology. Thesis no. 1434 (2010)

    Google Scholar 

  24. McKillup, S.: Statistics Explained: An Introductory Guide for Life Scientists. Cambridge University Press (2006)

    Google Scholar 

  25. Maji, P., Pal, S.: Maximum class separability for rough-fuzzy C-means based Brain MR image segmentation. T. Rough Sets 9, 114–134 (2008)

    Google Scholar 

  26. Mharib, A., Ramli, A., Mashohor, S., Mahmood, R.: Survey on liver CT image segmentation methods. Artif. Intell. Rev. 37(2), 83–95 (2012)

    Article  Google Scholar 

  27. Narayana, C., Sreenivasa Reddy, E., Seetharama Prasad M.: Automatic image segmentation using Ultrafuzziness. Int. J. Comput. Appl. 49(12), 6–13 (2012)

    Google Scholar 

  28. Oliveira, J.V., Pedrycz, W.: Advances in Fuzzy Clustering and Its Applications. Wiley, England (2007)

    Book  Google Scholar 

  29. Semechko. A.: Fast segmentation of N-dimensional grayscale images. http://www.mathworks.com/matlabcentral/fileexchange/41967-fast-segmentation-of-n-dimensional-grayscale-images. Accessed 8 Dec 2013

  30. Rutledge, R.: Just Enough SAS: A Quick-start Guide to SAS for Engineers. SAS Institute (2009)

    Google Scholar 

  31. Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 2(2), 171–195 (2008)

    Google Scholar 

  32. Zeman, R., Fox, S., Silverman, P., et al.: Helical (spiral) CT of the abdomen. AJR 160, 719–725 (1993)

    Article  Google Scholar 

  33. Zhou, X.-C.: Image segmentation based on modified particle Swarm optimization and fuzzy C-means Clustering algorithm. In: IEEE Computer Society Second International Conference on Intelligent Computation Technology and Automation, pp. 611–616. DC, USA (2009)

    Google Scholar 

  34. Zeman, R., Zeiberg, A., Davros, W., et al.: Routine helical CT of the abdomen: image quality considerations. Radiology 189, 395–400 (1993)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Abder-Rahman Ali, Ahmed Anter & Aboul-Ella Hassanien

  2. Institute of Systems and Robotics, Polo II, Pinhal de Marrocos, University of Coimbra, Coimbra, Portugal

    Micael Couceiro

  3. RoboCorp, Polytechnic Institute of Coimbra, DEE, Rua Pedro Nunes, Coimbra, Portugal

    Micael Couceiro

  4. Faculty of Computers and Information, Computer Science Department, Mansoura University, Mansoura, Egypt

    Ahmed Anter

  5. Faculty of Computers and Information, Computer Science Department, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

Authors
  1. Abder-Rahman Ali
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  2. Micael Couceiro
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  3. Ahmed Anter
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  4. Aboul-Ella Hassanien
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Corresponding author

Correspondence to Abder-Rahman Ali .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Information Systems and Computing, Brunel University, London, United Kingdom

    Crina Grosan

  3. Faculty of Computers and information, Ain Shams University, Cairo, Egypt

    Mohamed Fahmy Tolba

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Ali, AR., Couceiro, M., Anter, A., Hassanien, AE. (2016). Particle Swarm Optimization Based Fast Fuzzy C-Means Clustering for Liver CT Segmentation. In: Hassanien, AE., Grosan, C., Fahmy Tolba, M. (eds) Applications of Intelligent Optimization in Biology and Medicine. Intelligent Systems Reference Library, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-21212-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-21212-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21211-1

  • Online ISBN: 978-3-319-21212-8

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