Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis

  • Ahmed M. Abdeldaim
  • Ahmed T. Sahlol
  • Mohamed Elhoseny
  • Aboul Ella Hassanien
Part of the Studies in Computational Intelligence book series (SCI, volume 730)


Leukemia is a kind of cancer that basically begins in the bone marrow. It is caused by excessive production of leukocytes that replace normal blood cells. This chapter presents Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis system based on image analysis. It presented to identify the cells ALL by segmenting each cell in the microscopic images, and then classify each segmented cell to be normal or affected. A well-known dataset was used in this chapter (ALL-IDB2). The dataset contains 260 cell images: 130 normal and 130 affected by ALL. The proposed system starts by segmenting the white blood cells. This process includes sub-processes such as conversion from RGB to CMYK color model, histogram equalization, thresholding by Zack technique, and background removal operation. Then some features were extracted from each cell, each of them represents aspects of a cell. The extracted features include color, texture, and shape features. Then each feature set was exposed to three data normalization techniques z-score, min-max, and grey-scaling to narrow down the gap between the features values. Finally, different classifiers were used to validate the proposed system. The proposed diagnosing system achieved acceptable accuracies when tested by well-known classifiers; however, K-NN achieved the best classification accuracy.


Leukemia Acute lymphoblastic leukemia (ALL) Image analysis and segmentation Data normalization 


  1. 1.
    Bennett, J.M., Catovsky, D., Daniel, M.T., Flandrin, G., Galton, D.A., Gralnick, H.R., et al.: Proposals for the classification of the acute leukemias. French–American–British (FAB) co-operative group. Br. J. Hematol. (1976)Google Scholar
  2. 2.
    Inaba, H., Greaves, M., Mullighan, C.G.: Acute lymphoblastic leukaemia. Lancet 381, 1943–1955 (2013)CrossRefGoogle Scholar
  3. 3.
    Putzu, L., Caocci, G., Di Ruberto, C.: Leucocyte classification for leukaemia detection using image processing techniques. Artif. Intell. Med. 62, 179–191 (2014)CrossRefGoogle Scholar
  4. 4.
    Qiu, H.N., Wong, C.K., Chu, I.M., Hu, S., Lam, C.W.: Muramyl dipeptide mediated activation of human bronchial epithelial cells interacting with basophils: a novel mechanism of airway inflammation. Clin. Exp. Immunol. 172, 81–94 (2013)CrossRefGoogle Scholar
  5. 5.
    Meeusen, E.N., Balic, A.: Do eosinophils have a role in the killing of helminth parasites?. Parasitol. Today 16, 95–101 (2000)Google Scholar
  6. 6.
    Kolaczkowska, E., Kubes, P.: Neutrophil recruitment and function in health and inflammation. Nat. Rev. Immunol. 13, 159–175 (2013)Google Scholar
  7. 7.
    Thompson, S.C., Bowen, K.M., Burton, R.C.: Sequential monitoring of peripheral blood lymphocyte subsets in rats. Cytometry 7, 184–193 (1986)Google Scholar
  8. 8.
    Brown, A.L., Zhu, X., Rong, S., Shewale, S., Seo, J., et al.: Omega-3 fatty acids ameliorate atherosclerosis by favorably altering monocyte subsets and limiting monocyte recruitment to aortic lesions. Arterioscler. Thromb. Vasc. Biol. 32, 2122–2130 (2012)CrossRefGoogle Scholar
  9. 9.
    DonidaLabati, R., Piuri, V., Scotti, F.: ALL-IDB: The acute lymphoblastic leukemia image data base for image processing. In: The 18th IEEE International Conference on Image Processing (ICIP), pp. 2045–2048 (2011)Google Scholar
  10. 10.
    Mostafa, A., Fouad, A., Elfattah, M.A., Hassanien, A.E., Hefny, H., Zhu, S.Y., Schaefer, G.: CT liver segmentation using artificial bee colony optimisation. Procedia Comput. Sci. 60, 1622–1630 (2015)Google Scholar
  11. 11.
    Zidan, A., Ghali, N.I., Hassanien, A.E., Hefny, H., Hemanth, J.: Level set-based CT liver computer aided diagnosis system. J. Intell. Robot. Syst. 7, Number S13 (2012)Google Scholar
  12. 12.
    Anter, A.M., Hassenian, A.E., ElSoud, M.A., Tolba, M.F.: Neutrosophic sets and fuzzy C-means clustering for improving CT liver image segmentation. In: The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, Ostrava, Czech Republic, 22–24 June 2014Google Scholar
  13. 13.
    Sahlol, A.T., Suen, C.Y., Zawbaa, H.M., Hassanien, A.E., Elfattah, M.A.: Bio-inspired BAT optimization algorithm for handwritten arabic characters recognition. In: 2016 IEEE Congress on Evolutionary Computation (WCCI-2016), Vancouver, Canada, pp. 1749–1756, 22–24 July 2016Google Scholar
  14. 14.
    Elhoseny, M., Farouk, A., Batle, J., Abouhawwash, M., Hassanien, A.E.: Secure image processing and transmission schema in cluster-based wireless sensor network. In: Handbook of Research on Machine Learning Innovations and Trends (2017)Google Scholar
  15. 15.
    Mohapatra, S., Patra, D., Satpathy, S.: An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. J. Neural Comput. Appl. 24(7–8), 1887–1904 (2014)Google Scholar
  16. 16.
    Mohammed, R., Nomir, O., Khalifa, I.: Segmentation of acute lymphoblastic leukemia using C-Y color space. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 5(11), 99–101 (2014)Google Scholar
  17. 17.
    Prinyakupt, J., Pluempitiwiriyawej, C.: Segmentation of white blood cells and comparison of cell morphology by linear and naíve Bayes classifiers. Biomed. Eng. OnLine 14(1), 63 (2015)CrossRefGoogle Scholar
  18. 18.
    Halim, N.H.A., Mashor, M.Y., Hassan, R.: Automatic blasts counting for acute leukemia based on blood samples. Int. J. Res. Rev. Comput. Sci. 2(4), 971–976 (2011)Google Scholar
  19. 19.
    Biondi, A., Cimino, G., Pieters, R., Pui, C.H.: Biological and therapeutic aspects of infant leukemia. Blood 96, 24–33 (2000)Google Scholar
  20. 20.
    Zack, G., Rogers, W., Latt, S.: Automatic measurement of sister chromatid exchange frequency. J Histochem. Cytochem. 25(7), 741–753 (1977)CrossRefGoogle Scholar
  21. 21.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967)CrossRefMATHGoogle Scholar
  22. 22.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)MATHGoogle Scholar
  23. 23.
    Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Rosenbloom, Paul, Szolovits, Peter (eds.) Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 223–228. The AAAI Press, Menlo Park, CA (1992)Google Scholar
  24. 24.
    Vapnik, V.: Statistical Learning Theory. Wiley (1998)Google Scholar
  25. 25.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. (1986)Google Scholar
  26. 26.
    Sahlol, A.T., Suen, C.Y., Zawbaa, H.M., Hassanien, A.A., AbdElfattah, M.: Bio-inspired BAT optimization technique for handwritten Arabic characters recognition. In: IEEE Congress on Evolutionary Computation (WCCI-2016), Vancouver, Canada, pp. 1749–1756 (2016)Google Scholar
  27. 27.
    Sahlol, A.T., AbdElfattah, M., Suen, C.Y., Hassanien, A.A.: Particle swarm optimization with random forests for handwritten Arabic recognition system. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics (AISI 2016), Cairo, Egypt, pp. 437–446 (2016)Google Scholar
  28. 28.
    Sahlol, A.T., Suen, C.Y., Elbasyoni, M.R., Sallam, A.A.: Investigating of preprocessing techniques and novel features in recognition of handwritten Arabic characters. In: Artificial Neural Networks in Pattern Recognition, pp. 264–276. Springer International Publishing (2014)Google Scholar
  29. 29.
    Soille, P.: Morphological Image Analysis: Principles and Applications, pp. 170–171. Springer (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ahmed M. Abdeldaim
    • 1
    • 5
  • Ahmed T. Sahlol
    • 2
    • 5
  • Mohamed Elhoseny
    • 3
    • 5
  • Aboul Ella Hassanien
    • 4
    • 5
  1. 1.Culture & Science City6th of OctoberEgypt
  2. 2.Damietta UniversityDamiettaEgypt
  3. 3.Mansoura UniversityMansouraEgypt
  4. 4.Faculty of Computers and Information, Information Technology DepartmentCairo UniversityGizaEgypt
  5. 5.Scientific Research Group in Egypt (SRGE)CairoEgypt

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