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Leukocyte Detection Through an Evolutionary Method

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Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ,volume 319)


Classical image processing methods often face great difficulties while dealing with images containing noise and distortions. Under such conditions, the use of soft computing approaches has been recently extended to address challenging real-world image processing problems. The automatic detection of Leukocytes or White Blood Cells (WBC) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and image processing alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize such elements. This chapter presents an algorithm for the automatic detection of leukocytes embedded into complicated and cluttered smear images that considers the complete process as a multi-ellipse detection problem. The approach, which is based on the Differential Evolution (DE) algorithm, transforms the detection task into an optimization problem whose individuals represent candidate ellipses. An objective function evaluates if such candidate ellipses are actually present in the edge map of the smear image. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the leukocytes which are enclosed within the edge map of the smear image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of its accuracy and robustness.


  • Leukocyte detection
  • Image processing
  • WBC image analysis
  • Differential evolution
  • Evolutionary algorithms
  • Metaheuristics

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  • Atherton, T., & Kerbyson, D. (1993). Using phase to represent radius in the coherent circle Hough transform. In IEE Colloquium on the Hough Transform (pp. 1–4), May 7 1993, IEEE.

    Google Scholar 

  • Ayala-Ramirez, V., Garcia-Capulin, C. H., Perez-Garcia, A., & Sanchez-Yanez, R. E. (2006). Circle detection on images using genetic algorithms. Pattern Recognition Letters, 27(6), 652–657.

    CrossRef  Google Scholar 

  • Babu, B., & Munawar, S. (2007). Differential evolution strategies for optimal design of shell-and-tube heat exchangers. Chemical Engineering Science, 62(14), 3720–3739.

    CrossRef  Google Scholar 

  • Boccignone, G., Ferraro, M., & Napoletano, P. (2004). Diffused expectation maximisation for image segmentation. Electron Letters, 40(18), 1107–1108.

    CrossRef  Google Scholar 

  • Boccignone, G., Napoletano, P., Caggiano, V., & Ferraro, M. (2007). A multi-resolution diffused expectation–maximization algorithm for medical image segmentation. Computers in Biology and Medicine, 37(1), 83–96.

    CrossRef  Google Scholar 

  • Bresenham, J. E. (1987). A linear algorithm for incremental digital display of circular arcs. Communications of the ACM, 20(2), 100–106.

    CrossRef  Google Scholar 

  • Cheng, H. D., Guo, Y., & Zhang, Y. (2009). A novel Hough transform based on eliminating particle swarm optimization and its applications. Pattern Recognition, 42(9), 1959–1969.

    CrossRef  MATH  Google Scholar 

  • Chiou, J., Chang, C., & Su, C. (2005). Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems. IEEE Transactions on Power Systems, 20(2), 668–674.

    CrossRef  Google Scholar 

  • Cuevas, E., Zaldivar, D., & Pérez-Cisneros, M. (2010). A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Systems with Applications, 37(7), 5265–5271.

    CrossRef  Google Scholar 

  • DEM: Diffused expectation maximization function for image segmentation. (2012). Version 1.0.

  • Fischer, M., & Bolles, R. (1981). Random sample consensus: A paradigm to model fitting with applications to image analysis and automated cartography. CACM, 24(6), 381–395.

    CrossRef  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing. Reading, MA: Addison Wesley.

    Google Scholar 

  • Han, J., Koczy, L., & Poston, T. (1993). Fuzzy Hough transform. In Proceedings 2nd International Conference on Fuzzy Systems, San Francisco, California (Vol. 2, pp. 803–808), March 28–April 01 1993. doi:10.1109/FUZZY.1993.32.7545.

  • Kannan, S., Slochanal, S. M. R., & Padhy, N. (2003). Application and comparison of metaheuristic techniques to generation expansion planning problem. IEEE Transactions on Power Systems, 20(1), 466–475.

    CrossRef  Google Scholar 

  • Karkavitsas, G., & Rangoussi, M. (2005). Object localization in medical images using genetic algorithms. International Journal of Medical, Dentistry, Pharmaceutical, Health Science and Engineering, 1(2), 6–9.

    Google Scholar 

  • Landi, G., & Piccolomini, E. L. (2012). An efficient method for nonnegatively constrained total variation-based denoising of medical images corrupted by Poisson noise. Computerized Medical Imaging and Graphics, 36(1), 38–46.

    CrossRef  Google Scholar 

  • Lutton, E., & Martinez, P. (1994). A genetic algorithm for the detection of 2D geometric primitives in images. In Proceedings of the 12th International Conference On Pattern Recognition, Jerusalem, Israel (Vol. 1, pp. 9–13, 526–528), October 1994. doi:10.1109/ICPR.1994.576345.

  • Mayer, D., Kinghorn, B., & Archer, A. (2005). Differential evolution—An easy and efficient evolutionary algorithm for model optimization. Agricultural Systems, 83(3), 315–328.

    CrossRef  Google Scholar 

  • Muammar, H., & Nixon, M. (1989). Approaches to extending the Hough transform. In Proceedings International Conference on Acoustics, Speech and Signal Processing ICASSP-89, Glasgow (Vol. 3, pp. 23–26, 1556–1559), May 1989. doi:10.1109/ICASSP.1989.266739.

  • Scholl, I., Aach, T., Deserno, T. M., & Kuhlen, T. (2011). Challenges of medical image processing. Computer Science Research and Development., 26(1–2), 5–13.

    CrossRef  Google Scholar 

  • Shaked, D., Yaron, O., & Kiryati, N. (1996). Deriving stopping rules for the probabilistic Hough transform by sequential analysis. Computer Vision Image Understanding, 63(3), 512–526.

    CrossRef  Google Scholar 

  • Storn, R., & Price, K. (1995). Differential evolution—A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report No. TR-95-012, International Computer Science Institute, Berkley (CA).

    Google Scholar 

  • Tapiovaara, M., & Wagner, R. (1993). SNR and noise measurements for medical imaging: I. A practical approach based on statistical decision theory. Physics in Medicine and Biology, 38(1), 71–92.

    CrossRef  Google Scholar 

  • Van Aken, J. R. (2005). Efficient ellipse-drawing algorithm. IEEE Computer Graphics and Applications, 4(9), 24–35.

    CrossRef  Google Scholar 

  • Wang, M., & Chu, R. (2009). A novel white blood cell detection method based on boundary support vectors. Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA (pp. 2595–2598), October 11–14, 2009. DOI:10.1109/ICSMC.2009.5346736.

  • Wang, L., & Huang, F. (2010). Parameter analysis based on stochastic model for differential evolution algorithm. Applied Mathematics and Computation, 217(7), 3263–3273.

    CrossRef  MATH  MathSciNet  Google Scholar 

  • Wang, S., Korris, F. L., & Fu, D. (2007). Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection. Neurocomputing, 70(7–9), 1348–1359.

    Google Scholar 

  • Wu, J., Zeng, P., Zhou, Y., & Oliver, C. (2006). A novel color image segmentation method and its application to white blood cell image analysis. In 8th International Conference on Signal Processing, Beijing, China (Vol. 2, pp. 16–20, 16–20), November 2006. DOI:10.1109/ICOSP.2006.345700.

  • Xu, L., Oja, E., & Kultanen, P. (1990). A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Letters, 11(5), 331–338.

    CrossRef  MATH  Google Scholar 

  • Yao, J., Kharma, N., & Grogono, P. (2005). A multi-population genetic algorithm for robust and fast ellipse detection. Pattern Analysis Applications, 8(1–2), 149–162.

    CrossRef  MathSciNet  Google Scholar 

  • Zhuang, X., & Meng, Q. (2004). Local fuzzy fractal dimension and its application in medical image processing. Artificial Intelligence in Medicine, 32(1), 29–36.

    CrossRef  Google Scholar 

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Correspondence to Erik Cuevas .

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Cuevas, E., Díaz, M., Rojas, R. (2015). Leukocyte Detection Through an Evolutionary Method. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham.

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