ArticleApplied soft computing2020
Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation.
Article in Applied soft computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation.Applied soft computing · 2022Article
- An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.IEEE transactions on fuzzy systems : a publication of the IEEE Neural Networks Council · 2022Article
- A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach.Applied soft computing · 2022Article
- SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation.Expert systems with applications · 2021Article
- A morphology-based radiological image segmentation approach for efficient screening of COVID-19.Biomedical signal processing and control · 2021Article
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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
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