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Biomedical Image Segmentation Using Fuzzy Artificial Cell Swarm Optimization (FACSO)

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Abstract

This article describes a novel unsupervised approach to segmenting biomedical images. The proposed approach will be known as Fuzzy Artificial Cell Swarm Optimization. Artificial cell swarm optimization is one of the newest metaheuristic optimization procedures, which is not widely been applied and studied to date. The proposed approach extends the concept of artificial cell swarm optimization to the domain of fuzzy segmentation with the help of a type 2 fuzzy system. The proposed approach is robust and not dependent on the initial choice of the cluster centers. The proposed approach is applied to the biomedical images and compared with the advanced versions of some of the metaheuristic procedures like GA, PSO, ACO, and the artificial cell swarm optimization itself, using both qualitative and quantitative measures. Experimental results prove the efficiency and establish the practical applicability of the proposed approach to enhance computer-aided automated diagnostic systems.

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Acknowledgements

The authors would like to express their gratitude and thank the editors, anonymous reviewers, and referees for their valuable comments and suggestions which are helpful in further improvement of this research work.

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  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, India

    Shouvik Chakraborty & Kalyani Mali

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  1. Shouvik Chakraborty
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Correspondence to Shouvik Chakraborty.

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Chakraborty, S., Mali, K. Biomedical Image Segmentation Using Fuzzy Artificial Cell Swarm Optimization (FACSO). Neural Process Lett 55, 5215–5243 (2023). https://doi.org/10.1007/s11063-022-11088-x

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