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

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 $$ index are used for the comparison and evaluation purpose. For the GA, PSO, ACO, EMO and the proposed FEMO approach, the optimal average value of the Davies-Bouldin index is 1.833578359 (8 clusters), 1.669359475 (3 clusters), 1.623119284 (3 clusters), 1.647743907 (4 clusters) and 1.456889343 (3 clusters) respectively. It shows that the proposed approach can efficiently determine the optimal clusters. Moreover, the results of the other quantitative indices are quite promising for the proposed approach compared to the other approaches The detailed comparison is performed in both qualitative and quantitative manner and it is found that the proposed method outperforms some of the existing methods concerning some standard evaluation parameters.