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Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization

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The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 407))

Abstract

Image segmentation is an important task in the image processing field. Efficient segmentation of images considered important for further object recognition and classification. This paper presents a novel segmentation approach based on Particle Swarm Optimization (PSO) and an adaptive Watershed algorithm. An application of liver CT imaging has been chosen and PSO approach has been applied to segment abdominal CT images. The experimental results show the efficiency of the proposed approach and it obtains overall accuracy 94 % of good liver extraction.

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References

  1. Aly, A., Deris, S., Zaki, N.: Research review for digital image segmentation techniques. Int. J. Comput. Sci. Inf. Technol. 3(5), 99–106 (2011)

    Google Scholar 

  2. Islam, S., Ahmed, M.: Implementation of image segmentation for natural images using clustering methods. Int. J. Emerg. Technol. Adv. Eng. 3(3), 175–180 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  4. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

    Article  Google Scholar 

  5. Ghamisi, P., Couceiro, M., Benediktsson, J., Ferreira, N.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. Elsevier 39(16), 12407–12417 (2012)

    Article  Google Scholar 

  6. Abdalla, Z., Neveen, I.G., Aboul Ella, H., Hesham, A.H.: Level set-based CT liver image segmentation with watershed and artificial neural networks, IEEE, pp 96–102 (2012)

    Google Scholar 

  7. Aldeek, N., Alomari, R., Al-Zoubi, M., Hiary, H.: Liver segmentation from abdomen ct images with bayesian model. J. Theor. Appl. Inform. Technol. 60(3), 483–490 (2014)

    Google Scholar 

  8. Hammouche, K., Diaf, M., Siarry, P.: A comparative study of various metaheuristic techniques applied to the multilevel thresholding problem. Eng. Appl. Artif. Intell. 23, 676–688 (2010)

    Google Scholar 

  9. Sakthivel, N., Prabhu2, L.: Mean median filtering for impulsive noise removal. Int. J. Basics Appl. Sci. 2(4), 47–57 (2014)

    Google Scholar 

  10. Leavline, EJ., Singh, DAAG.: Salt and pepper noise detection and removal in gray scale images: an experimental analysis. Int. J. Signal Process. Image Process. Pattern Recognit. 6(5), 343–352 (2013)

    Google Scholar 

  11. Salman, N.: Image segmentation based on watershed and edge detection techniques. Int. Arab J. Inf. Tech. 3(4), 104–110 (2006)

    Google Scholar 

  12. Kaur, A., Aayushi.: Image segmentation using watershed transform. Int. J. Soft Comput. Eng. (IJSCE) 4(1), 5–8 (2014)

    Google Scholar 

  13. Anter, A., Azar, A., Hassanien, A., El-Bendary, N., El-Soud, 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 

  14. Abdalla, M., Hesham, H., Neven, I.G., Aboul Ella, H., Gerald, S.: Evaluating the effects of image filters in CT liver CAD system. In: proceeding of IEEE-EMBS International Conference on Biomedical and Health Informatics, The Chinese University of Hong Kong, Hong Kong (2012)

    Google Scholar 

  15. Ruchaneewan, S., Daniela, S., Jacob, F.: A hybrid approach for liver segmentation, intelligent multimedia processing laboratory, 3d segmentation in the clinic: a grand challenge, pp 151–160 (2007)

    Google Scholar 

  16. Jeongjin, L., Namkug, K., Ho, L., Joon, B., Hyung, J., Yong, M., Yeong, S., Soo Hong, K.: Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images, Elsevier, computer 1 methods and programs in biomedicine, vol. 88, pp. 26–38 (2007)

    Google Scholar 

  17. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)

    Article  Google Scholar 

  18. Chakraborty, S., Samanta, S., Biswas, D., Dey, N., Chaudhuri, S.S.: Particle swarm optimization based parameter optimization technique in medical information hiding. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) pp. 1–6 (2013)

    Google Scholar 

  19. Hu, W., Yen, G.G.: Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system, IEEE Trans. Evol. Comput. 19(1), 1–18 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benisuef University, Beni Suef, Egypt

    Aboul Ella Hassanien

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

    Gehad Ismail Sayed & Aboul Ella Hassanien

Authors
  1. Gehad Ismail Sayed
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  2. Aboul Ella Hassanien
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Corresponding author

Correspondence to Gehad Ismail Sayed .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  2. Department of Information Technology, Cairo University Faculty of Computers & Information, Giza, Egypt

    Aboul Ella Hassanien

  3. College of Management and Technology, Arab Academy for Science, Technology, and Maritime Transport, Giza, Egypt

    Nashwa El-Bendary

  4. Bengal College of Engineering and Technology, Durgapur, West Bengal, India

    Nilanjan Dey

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Sayed, G.I., Hassanien, A.E. (2016). Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-26690-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26688-6

  • Online ISBN: 978-3-319-26690-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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