Abstract
As a research area, there are several problems in medical imaging that continue unresolved; one of those is the automatic detection of white blood cells (WBC) in smear images. The study of this kind of images has engaged researchers from fields of medicine and computer vision alike. Several studies have been done to try to approximate this cells with circular or ellipsoid forms; once detected, those cells can be further processed by computer vision systems. In this chapter, detection of WBC in smear digitalized images is achieved by using evolutionary algorithms, with an objective function that considers that since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize them. In that sense, the optimization problem also consider that a candidate solution is a probable ellipse that could adjust a WBC in the image.
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Cuevas, E., Osuna, V., Oliva, D. (2017). White Blood Cells Detection in Images. In: Evolutionary Computation Techniques: A Comparative Perspective. Studies in Computational Intelligence, vol 686. Springer, Cham. https://doi.org/10.1007/978-3-319-51109-2_8
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