Advertisement

Cell Blood Image Segmentation Based on Genetic Algorithm

  • A. Y. Ayoub
  • M. A. El-ShorbagyEmail author
  • I. M. El-Desoky
  • A. A. Mousa
Conference paper
  • 151 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

There are many methods of clustering that have been used to divide images. k-means algorithm is considered the most popular method of cluster analysis. Due to disadvantages of k-means algorithm, in this paper, the image is segmented using modified genetic algorithm (GA) by k-means algorithm; where k-means is used as an initialization of GA. The proposed algorithm was applied to several of cell blood images from microscope and the results showed that the value of PSNR for the proposed algorithm is higher than other algorithms, which indicates its efficiency in image segmentation.

Keywords

Image segmentation Genetic algorithm Cluster analysis k-means algorithm 

References

  1. 1.
    Norouzi, A., Rahim, M.S., Altameem, A., Saba, T., Rad, A.E., Rehman, A., Uddin, M.: Medical image segmentation methods, algorithms, and applications. IETE Tech. Rev. 31, 199–213 (2014)CrossRefGoogle Scholar
  2. 2.
    Jose, A., Ravi, S., Sambath, M.: Brain tumor segmentation using k-means clustering and fuzzy C-means algorithms and its area calculation. Int. J. Innov. Res. Comput. Commun. Eng. 2, 3496–3501 (2014)Google Scholar
  3. 3.
    Khan, J.F., Bhuiyan, S.M.A., Adhami, R.R.: Image segmentation and shape analysis for road-sign detection. IEEE Trans. Intell. Transp. Syst. 12, 83–96 (2011)CrossRefGoogle Scholar
  4. 4.
    Brunoa, L., Parla, G., Celauro, C.: Improved traffic signal detection and classification via image processing. Soc. Behav. Sci. 53, 811–821 (2012)Google Scholar
  5. 5.
    Dhanachandra, N., Manglem, K., Chanu, Y.J.: Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015)CrossRefGoogle Scholar
  6. 6.
    Singh, K., Malik, D., Sharma, N.: Evolving limitations in k-means algorithm in data mining and their removal. IJCEM Int. J. Comput. Eng. Manag. 12, 105–109 (2011)Google Scholar
  7. 7.
    Bosco, G.L.: A genetic algorithm for image segmentation. In: Proceedings 11th International Conference on Image Analysis and Processing, Palermo, Italy (2001)Google Scholar
  8. 8.
    Amelio, A., Pizzuti, C.: A genetic algorithm for color image segmentation. In: Proceedings of the 16th European conference on Applications of Evolutionary Computation, vol. 1, pp. 314–323 (2013)Google Scholar
  9. 9.
    Gautam, K., Singhai, R.: Color image segmentation using particle swarm optimization in lab color space. Int. J. Eng. Dev. Res. (2018 IJEDR) 6, 373–377 (2018)Google Scholar
  10. 10.
    Dhanalakshm, L., Ranjitha, S., Suresh, H.N.: A novel method for image processing using particle swarm optimization technique. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India (2016)Google Scholar
  11. 11.
    Gupta, K., Gupta, A.: Image enhancement using ant colony optimization. IOSR J. VLSI Signal Process. (IOSR-JVSP) 1, 38–45 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, X., Feng, Y., Feng, Z.: Ant colony optimization for image segmentation. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou (2005)Google Scholar
  13. 13.
    Chen, M., Ludwig, S.A.: Color image segmentation using fuzzy C-regression mode. Adv. Fuzzy Syst. 2, 1–15 (2017)Google Scholar
  14. 14.
    Li, C., Liu, L., Sun, X., Zhao, J., Yin, J.: Image segmentation based on fuzzy clustering with cellular automata and features weighting. EURASIP J. Image Video Process. 2019, 1–11 (2019)CrossRefGoogle Scholar
  15. 15.
    Pei, Z., Zhao, Y., Liu, Z.: Image segmentation based on differential evolution algorithm. In: 2009 International Conference on Image Analysis and Signal Processing, Taizhou, China (2009)Google Scholar
  16. 16.
    Sarkar, S., Patra, G.R., Das, S.: A differential evolution based approach for multilevel image segmentation using minimum cross entropy thresholding. In: International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011), Visakhapatnam, India (2011)Google Scholar
  17. 17.
    Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognit. 33, 1455–1465 (2000)CrossRefGoogle Scholar
  18. 18.
    El-shorbagy, M.A., Ayoub, A.Y., El-desoky, I.M., Mousa, A.A.: A novel genetic algorithm based k-means algorithm for cluster analysis. In: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo (2018)Google Scholar
  19. 19.
    El-shorbagy, M.A., Ayoub, A.Y., Mousa, A.A., El-desoky, I.M.: An enhanced genetic algorithm with new mutation for cluster analysis. Comput. Stat. 34, 1355–1392 (2019)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Abdelsalam, A.M., El-Shorbagy, M.A.: Optimization of wind turbines siting in a wind farm using genetic algorithm based. Renew. Energy. 123, 748–755 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • A. Y. Ayoub
    • 1
  • M. A. El-Shorbagy
    • 1
    • 2
    Email author
  • I. M. El-Desoky
    • 1
  • A. A. Mousa
    • 1
    • 3
  1. 1.Department of Basic Engineering Science, Faculty of EngineeringMenoufia UniversityShebin El-KomEgypt
  2. 2.Department of Mathematics, College of Science and Humanities in Al-KharjPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia
  3. 3.Mathematics and Statistics Department, College of ScienceTaif UniversityTaifSaudi Arabia

Personalised recommendations