Nature Inspired Optimization Algorithms for CT Liver Segmentation

Ahmed Fouad Ali,Abdalla Mostafa,Gehad Ismail Sayed,Aboul Ella Hassanien,Mohamed Abd Elfattah

doi:10.1007/978-3-319-33793-7_19

Nature Inspired Optimization Algorithms for CT Liver Segmentation

Ahmed Fouad Ali, Abdalla Mostafa + Show 3 more

https://doi.org/10.1007/978-3-319-33793-7_19

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Publication Date: Jan 1, 2016

Citations: 13

Affiliation: Scientific Research Group in Egypt, Suez Canal University, Cairo University, Mansoura University

#Nature Inspired Optimization Algorithms #Social Spider Optimization + Show 8 more

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Abstract

Nature inspired optimization algorithms have gained popularity in the last two decades due to their efficiency and flexibility when they applied to solve global optimization problems. These algorithms are inspired from the biological behavior by swarms of birds, fish and bees. In this chapter, we give an overview of some of nature inspired optimization algorithms such as Artificial Bee Colony (ABC), Cuckoo Search (CS), Social Spider Optimization (SSO) and Grey Wolf Optimization (GWO). Also, we present the usage of ABC and GWO algorithms for CT liver segmentation. The experimental results of the two selected algorithms show that the two algorithms are powerful and can obtain good results when applied to segment medical images.

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