Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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.
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.
Babu, B., & Munawar, S. (2007). Differential evolution strategies for optimal design of shell-and-tube heat exchangers. Chemical Engineering Science, 62(14), 3720–3739.
Boccignone, G., Ferraro, M., & Napoletano, P. (2004). Diffused expectation maximisation for image segmentation. Electron Letters, 40(18), 1107–1108.
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.
Bresenham, J. E. (1987). A linear algorithm for incremental digital display of circular arcs. Communications of the ACM, 20(2), 100–106.
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.
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.
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.
DEM: Diffused expectation maximization function for image segmentation. (2012). Version 1.0. http://www.mathworks.com/matlabcentral/fileexchange/37197-dem-diffused-expectation-maximisation-for-image-segmentation.
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.
Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing. Reading, MA: Addison Wesley.
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.
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.
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.
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.
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.
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.
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).
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.
Van Aken, J. R. (2005). Efficient ellipse-drawing algorithm. IEEE Computer Graphics and Applications, 4(9), 24–35.
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.
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.
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.
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.
Zhuang, X., & Meng, Q. (2004). Local fuzzy fractal dimension and its application in medical image processing. Artificial Intelligence in Medicine, 32(1), 29–36.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
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. https://doi.org/10.1007/978-3-319-12883-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-12883-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12882-5
Online ISBN: 978-3-319-12883-2
eBook Packages: EngineeringEngineering (R0)