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Optimized control for medical image segmentation: improved multi-agent systems agreements using Particle Swarm Optimization

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Abstract

The optimal segmentation of medical images remains important for promoting the intensive use of automatic approaches in decision making, disease diagnosis, and facilitating the sustainable development of computer vision studies. Generally, recent methods tend to minimize human–machine interaction by using multi-agent systems (MAS) and optimize the segmentation systems control. Some of the existing segmentation methods consider MAS qualifications and advantages but underline a lack of global optimization goals, and therefore they provide unsatisfactory results taking into account the need for precision in medical imaging. Our work coupled an improved MAS control protocol for medical image segmentation with the particle swarm optimization algorithm to strengthen the system for better result performance. The proposed method could relieve agents’ conflicts during the medical image segmentation for optimum control, better decision-making, and higher processing quality under the critical medical restrictions.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, Morocco

    Hanane Allioui, Mohamed Sadgal & Aziz Elfazziki

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  1. Hanane Allioui
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  2. Mohamed Sadgal
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Correspondence to Hanane Allioui.

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Allioui, H., Sadgal, M. & Elfazziki, A. Optimized control for medical image segmentation: improved multi-agent systems agreements using Particle Swarm Optimization. J Ambient Intell Human Comput 12, 8867–8885 (2021). https://doi.org/10.1007/s12652-020-02682-9

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