Abstract
There is a remarkable increase in the popularity of swarms inspired algorithms in the last decade. It offers a kind of flexibility and efficiency in their applications in different fields. These algorithms are inspired by the behaviour of various swarms as birds, fish and animals. This chapter presents an overview of some algorithms as grey wolf optimization (GWO), artificial bee colony (ABC) and antlion optimization (ALO). It proposed swarm optimization approaches for liver segmentation based on these algorithms in CT and MRI images. The experimental results of these algorithms show that they are powerful and can get remarkable results when applied to segment liver medical images. It is evidently proved from the experimental results that ALO, GWO and ABC have obtained 94.49%, 94.08% and 93.73%, respectively, in terms of overall accuracy using similarity index measure.
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Mostafa, A., Houssein, E.H., Houseni, M., Hassanien, A.E., Hefny, H. (2018). Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images. In: Hassanien, A., Oliva, D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_3
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