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Improved African vultures optimization algorithm for medical image segmentation

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Abstract

Multi-level threshold image segmentation is widely used in medical image segmentation. Traditional methods for selecting optimal thresholds suffer from exponentially increasing time complexity as the number of threshold levels increases. In order to solve these problems, we choose African vultures optimization algorithm (AVOA) and introduce a novel modified African vultures optimization algorithm method called OLAVOA that combines predatory memory and logarithmic spiral based on opposition learning. In addition, we also apply 2D Kapur's entropy as a fitness value function of OLAVOA for multi-threshold image segmentation to solve the problem of traditional method. In the 30 benchmark function experiments at IEEE CEC2014, the average value of the experimental results of OLAVOA mostly outperforms the other algorithms. In the experimental convergence graph, OLAVOA's performance showed its convergence’s velocity and capacity to depart from the local best. In addition, to demonstrate the effectiveness of OLAVOA on medical image segmentation, the image segmentation experiment included chest X-ray image of patients with COVID-19 and brain MRI image. Additionally, it was demonstrated that OLAVOA outperformed other approaches in segmentation trials by having greater adaptability in different threshold levels. Therefore, OLAVOA is effectively utilized to segment medical images.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (62376106), the Innovation Capacity Construction Project of Jilin Province Development and Reform Commission (2021FGWCXNLJSSZ10, 2019C053-3), the National Key Research and Development Program of China (No. 2020YFA0714103) and the Fundamental Research Funds for the Central Universities, JLU.

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  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Lin Lan & Shengsheng Wang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Lin Lan & Shengsheng Wang

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Lan, L., Wang, S. Improved African vultures optimization algorithm for medical image segmentation. Multimed Tools Appl 83, 45241–45290 (2024). https://doi.org/10.1007/s11042-023-17189-6

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