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Leukocyte Detection by Using Electromagnetism-like Optimization

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Applications of Evolutionary Computation in Image Processing and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 100))

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Abstract

Automatic circle detection in digital images has been considered as an important and complex task for the computer vision community that has devoted important research efforts into optimal circle detectors. On the other hand, medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBC’s can be approximated by a quasi-circular form, a circular detector algorithm may be successfully applied. This chapter presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the Electromagnetism-Like Optimization (EMO) which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The EMO algorithm is based on the electromagnetic attraction and repulsion among charged particles whose charge represents the fitness solution for each particle (a given solution). The algorithm uses the encoding of three non-collinear edge points as candidate circles over an edge map. A new objective function has been derived to measure the resemblance of a candidate circle to an actual WBC based on the information from the edge map and segmentation results. Guided by the values of such objective function, the set of encoded candidate circles (charged particles) are evolved by using the EMO algorithm so that they can fit into the actual blood cells contained in the edge-only map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the presented technique regarding detection, robustness and stability.

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Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Av. Revolución 1500, Guadalajara, Mexico

    Erik Cuevas & Daniel Zaldívar

  2. Centro Universitario de Tonalá, Universidad de Guadalajara, Av. Nuevo Periférico No. 555 Ejido San José Tatepozco, 48525, Tonalá, Jalisco, Mexico

    Marco Perez-Cisneros

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  1. Erik Cuevas
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  2. Daniel Zaldívar
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  3. Marco Perez-Cisneros
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Correspondence to Erik Cuevas .

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Cuevas, E., Zaldívar, D., Perez-Cisneros, M. (2016). Leukocyte Detection by Using Electromagnetism-like Optimization. In: Applications of Evolutionary Computation in Image Processing and Pattern Recognition. Intelligent Systems Reference Library, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-26462-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-26462-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26460-8

  • Online ISBN: 978-3-319-26462-2

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