Skip to main content
SpringerLink
Log in

Segmentation of Blood Cell Images Using Evolutionary Methods

  • Conference paper
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II

Part of the book series: Advances in Intelligent Systems and Computing ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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)

    Article  Google Scholar 

  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. 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. 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. Donoho, D., Liu, R.: The automatic robustness of minimum distance functionals. Annals of Statistics 16, 552–586 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)

    Article  Google Scholar 

  12. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  19. Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  20. Park, H., Amari, S., Fukumizu, K.: Adaptive natural gradient learning algorithms for various stochastic models. Neural Networks 13, 755–764 (2000)

    Article  Google Scholar 

  21. Park, H., Ozeki, T.: Singularity and slow Convergence of the EM algorithm for Gaussian Mixtures. Neural Processing Letters 29, 45–59 (2009)

    Article  Google Scholar 

  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. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–168 (2004)

    Article  Google Scholar 

  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. Schmitt, F., Priese, L.: Sky detection in CSC color images. In: VISAPP2, pp. 101–106. INSTICC Press (2009)

    Google Scholar 

  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. Yang, X.-S.: Review of meta-heuristics and generalized evolutionary walk algorithm. Int. J. Bio-Inspired Comput. 3(2) (2011)

    Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. CIC-IPN, Juan de Dios Batiz S/N, Col. Nva. Industrial Vallejo, Mexico City, Mexico

    Valentín Osuna & Humberto Sossa

  2. Dept. de C. Comput, CUCEI-UDEG, Av. Revolucin 1500, Guadalajara, Jal, Mexico

    Erik Cuevas

Authors
  1. Valentín Osuna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Humberto Sossa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentín Osuna .

Editor information

Editors and Affiliations

  1. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Oliver Schütze

  2. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Carlos A. Coello Coello

  3. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  4. Computer Science and Communications, Research Unit, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Emilia Tantar

  5. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Pascal Bouvry

  6. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

  7. Bâtiment Leyteire, UFR Sciences et Modélisation, Université Bordeaux II, 3ter place de la Victoire, Bordeaux, 33000, France

    Pierrick Legrand

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Osuna, V., Cuevas, E., Sossa, H. (2013). Segmentation of Blood Cell Images Using Evolutionary Methods. In: Schütze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31519-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31519-0_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31518-3

  • Online ISBN: 978-3-642-31519-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation