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Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation

ABSTRACT: In this work, a new unsupervised classification approach is proposed for the biomedical image segmentation. The proposed method will be known as Fuzzy Electromagnetism Optimization (FEMO). As the name suggests, the proposed approach is based on the electromagnetism-like optimization (EMO) method. The EMO method is extended, modified, and combined with the modified type 2 fuzzy C-Means algorithm to improve its efficiency especially for biomedical image segmentation. The proposed FEMO method uses fuzzy membership and the electromagnetism-like optimization method to locate the optimal positions for the cluster centers. The proposed FEMO approach does not have any dependency on the initial selection of the cluster centers. Moreover, this method is suitable for the biomedical images of different modalities. This method is compared with some standard metaheuristics and evolutionary methods (e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Electromagnetism-like optimization (EMO), Ant Colony Optimization (ACO), etc.) based image segmentation approaches. Four different indices Davies–Bouldin, Xie–Beni, Dunn and Graphical abstract Highlights • FEMO removes the dependency on the choice of the initial cluster centers.• Several standard metaheuristic methods are outperformed by the proposed approach.• FEMO can efficiently find the global optima for the higher number of clusters.• The convergence of the FEMO is better than some other metaheuristic methods.• Experiments are carried out using both qualitative and quantitative measures.

SUBMITTER: Chakraborty S

PROVIDER: S-EPMC7566893 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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