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Segmentation of Blood Cell Images Using Evolutionary Methods

  • Valentín Osuna
  • Erik Cuevas
  • Humberto Sossa
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

Acute lymphoblastic leukemia is a blood cancer that can be cured if it is detected at early stages; however, the analysis of smear blood by a human expert is tired and subject to errors. In such a sense, diagnostic of the disease is costly and time consuming. Considering that situation, several automatic segmentation methods have been proposed, some of them containing combinations of classic image analysis tools, as thresholding, morphology, color segmentation and active contours, only to mention some. In this paper is proposed the use of Hellinger distance as an alternative to Euclidean distance in order to estimate a Gaussian functions mixture that better fits a gray-level histogram of blood cell images. Two evolutionary methods (Differential Evolution and Artificial Bee Colony) are used to perform segmentation based on histogram information and an estimator of minimum distance. The mentioned techniques are compared with classic Otsu’s method by using a qualitative measure of the resulting segmentation and ground-truth images. Experimental results show that the three methods performed almost in a similar fashion, but the evolutionary ones evaluate almost 75 % less the objective function compared with Otsu’s. Also, was found that the use of a minimum distance estimator constructed with Hellinger distance and evolutionary techniques is robust and does not need a penalization factor as the needed when an Euclidean distance is used.

Keywords

Differential Evolution Hausdorff Distance Hellinger Distance Minimum Distance Estimator Smear Blood Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Arora, S., Acharya, J., Verma, A., Panigrahi, P.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognition Letters 29, 119–125 (2008)CrossRefGoogle Scholar
  2. 2.
    Bissinger, B.E., Culver, R.L., Bose, N.K.: Minimum Hellinger Distance based classification of underwater acoustic signals. In: 43rd Annual Conference on Information Sciences and Systems, CISS 2009, March 18-20, pp. 47–49 (2009)Google Scholar
  3. 3.
    Cuevas, E., Zaldívar, D., Pérez-Cisneros, M.: A novel multi-threshold segmentation approach based on differential evolution optimization. Expert. Syst. Appl. 37(7), 5265–5271 (2010)Google Scholar
  4. 4.
    Labati, R., Donida Piuri, V., Scotti, F.: All-IDB: The acute lymphoblastic leukemia image database for image processing. In: 18th IEEE International Conference on Image Processing (ICIP 2011), September 11-14, pp. 2045–2048 (2011)Google Scholar
  5. 5.
    Donoho, D., Liu, R.: The automatic robustness of minimum distance functionals. Annals of Statistics 16, 552–586 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Dubuisson, M.-P., Jain, A.K.: A modified Hausdorff distance for object matching. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, October 9-13, vol. 1, pp. 566–568 (1994)Google Scholar
  7. 7.
    Halim, N.H.A., Mashor, M.Y., Abdul Nasir, A.S., Mokhtar, N.R., Rosline, H.: Nucleus segmentation technique for acute Leukemia. In: IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA), March 4-6, pp. 192–197 (2011)Google Scholar
  8. 8.
    Nor Hazlyna, H., Mashor, M.Y., Mokhtar, N.R., Aimi Salihah, A.N., Hassan, R., Raof, R.A.A., Osman, M.K.: Comparison of acute leukemia Image segmentation using HSI and RGB color space. In: 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA 2010), May 10-13, pp. 749–752 (2010)Google Scholar
  9. 9.
    Janev, M., Pekar, D., Jakovljevic, N., Delic, V.: Eigenvalues Driven Gaussian Selection in continuous speech recognition using HMMs with full covariance matrices. Applied Intelligence 33(2), 107–116 (2010)CrossRefGoogle Scholar
  10. 10.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  11. 11.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)CrossRefGoogle Scholar
  12. 12.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Kocsor, A., Tóth, L.: Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification. Applied Intelligence 21(2), 129–142 (2004)zbMATHCrossRefGoogle Scholar
  14. 14.
    Mezghani, N., Mitiche, A., Cheriet, M.: On-line character recognition using histograms of features and an associative memory. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), vol. 5, pp. 17–21 (2004)Google Scholar
  15. 15.
    Mohapatra, S., Patra, D.: Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images. In: International Conference on Systems in Medicine and Biology (ICSMB 2010), December 16-18, pp. 49–54 (2010)Google Scholar
  16. 16.
    Mohapatra, S., Samanta, S.S., Patra, D., Satpathi, S.: Fuzzy Based Blood Image Segmentation for Automated Leukemia Detection. In: International Conference on Devices and Communications (ICDeCom 2011), February 24-25, pp. 1–5 (2011)Google Scholar
  17. 17.
    Mohapatra, S., Patra, D., Kumar, K.: Blood microscopic image segmentation using rough sets. In: International Conference on Image Information Processing (ICIIP 2011), November 3-5, pp. 1–6 (2011)Google Scholar
  18. 18.
    Olsson, R., Petersen, K., Lehn-Schioler, T.: State-Space Models: From the EM Algorithm to a Gradient Approach. Neural Computation 19(4), 1097–1111 (2008)CrossRefGoogle Scholar
  19. 19.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Park, H., Amari, S., Fukumizu, K.: Adaptive natural gradient learning algorithms for various stochastic models. Neural Networks 13, 755–764 (2000)CrossRefGoogle Scholar
  21. 21.
    Park, H., Ozeki, T.: Singularity and slow Convergence of the EM algorithm for Gaussian Mixtures. Neural Processing Letters 29, 45–59 (2009)CrossRefGoogle Scholar
  22. 22.
    Umebayashi, K., Lehtomaki, J., Ruotsalainen, K.: Analysis of Minimum Hellinger Distance Identification for Digital Phase Modulation. In: IEEE International Conference on Communications (ICC 2006), vol. 7, pp. 2952–2956 (2006)Google Scholar
  23. 23.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–168 (2004)CrossRefGoogle Scholar
  24. 24.
    Storn, R., Price, K.: Differential evolution- a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report (1995)Google Scholar
  25. 25.
    Schmitt, F., Priese, L.: Sky detection in CSC color images. In: VISAPP2, pp. 101–106. INSTICC Press (2009)Google Scholar
  26. 26.
    Chen, W., Fang, K.: Multilevel thresholding algorithm based on particle swarm optimization for image segmentation. In: 27th Chinese Control Conference (CCC 2008), pp. 348–351 (2008)Google Scholar
  27. 27.
    Yang, X.-S.: Review of meta-heuristics and generalized evolutionary walk algorithm. Int. J. Bio-Inspired Comput. 3(2) (2011)Google Scholar
  28. 28.
    Zhiwei, Y., Zhengbing, H., Huamin, W., Hongwei, C.: Automatic Threshold Selection Based on Artificial Bee Colony Algorithm. In: 3rd International Workshop on Intelligent Systems and Applications (ISA), pp. 1–4 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.CIC-IPNMexico CityMexico
  2. 2.Dept. de C. ComputCUCEI-UDEGGuadalajaraMexico

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