IDRM: Brain tumor image segmentation with boosted RIME optimization

Wei Zhu; Liming Fang; Xia Ye; Mohamed Medani; José Escorcia-Gutierrez

doi:10.1016/j.compbiomed.2023.107551

IDRM: Brain tumor image segmentation with boosted RIME optimization

Comput Biol Med. 2023 Sep 30:166:107551. doi: 10.1016/j.compbiomed.2023.107551. Online ahead of print.

Authors

Wei Zhu¹, Liming Fang², Xia Ye³, Mohamed Medani⁴, José Escorcia-Gutierrez⁵

Affiliations

¹ School of Resources and Safety Engineering, Central South University, Changsha, 410083, China. Electronic address: csuzhuwei@csu.edu.cn.
² School of Humanities and Communication, Zhejiang Gongshang University, Hangzhou, 310000, China. Electronic address: flmdd@126.com.
³ School of the 1st Clinical Medical Sciences(School of Information and Engineering), Wenzhou Medical University, Wenzhou, 325000, China. Electronic address: yex@wmu.edu.cn.
⁴ Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil Aseer, 62529, Saudi Arabia. Electronic address: Mmedani@kku.edu.sa.
⁵ Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia. Electronic address: jescorci56@cuc.edu.co.

PMID: 37832284
DOI: 10.1016/j.compbiomed.2023.107551

Abstract

Timely diagnosis of medical conditions can significantly mitigate the risks they pose to human life. Consequently, there is an urgent demand for an effective auxiliary model that assists physicians in accurately diagnosing medical conditions based on imaging data. While multi-threshold image segmentation models have garnered considerable attention due to their simplicity and ease of implementation, the selection of threshold combinations greatly influences the segmentation performance. Traditional optimization algorithms often require substantial time to address multi-threshold image segmentation problems, and their segmentation accuracy is frequently unsatisfactory. As a result, metaheuristic algorithms have been employed in this domain. However, several algorithms suffer from drawbacks such as premature convergence and inadequate exploration of the solution space when it comes to threshold selection. For instance, the recently proposed optimization algorithm RIME, inspired by the physical phenomenon of rime-ice, falls short in terms of avoiding local optima and fully exploring the solution space. Therefore, this study introduces an enhanced version of RIME, called IDRM, which incorporates an interactive mechanism and Gaussian diffusion strategy. The interactive mechanism facilitates information exchange among agents, enabling them to evolve towards more promising directions and increasing the likelihood of discovering the optimal solution. Additionally, the Gaussian diffusion strategy enhances the agents' local exploration capabilities and expands their search within the solution space, effectively preventing them from becoming trapped in local optima. Experimental results on 30 benchmark test functions demonstrate that IDRM exhibits favorable optimization performance across various optimization functions, showcasing its robustness and convergence properties. Furthermore, the algorithm is applied to select threshold combinations for brain tumor image segmentation, and the results are evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The overall findings consistently highlight the exceptional performance of this approach, further validating the effectiveness of IDRM in addressing image segmentation problems.

Keywords: Brain tumor detection; Image segmentation; Meta-heuristic algorithms; Multi-threshold; RIME; Rényi's entropy.