An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images.

Autor: Abdel-Salam M; Faculty of Computers and Information Science, Mansoura University, Mansoura, Egypt. Electronic address: mahmoud20@mans.edu.eg., Houssein EH; Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address: essam.halim@mu.edu.eg., Emam MM; Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address: marwa.khalef@mu.edu.eg., Samee NA; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. Electronic address: nmabdelsamee@pnu.edu.sa., Jamjoom MM; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia. Electronic address: mmjamjoom@pnu.edu.sa., Hu G; Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China. Electronic address: hugang@xaut.edu.cn.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Oct 14; Vol. 183, pp. 109272. Date of Electronic Publication: 2024 Oct 14.
DOI: 10.1016/j.compbiomed.2024.109272
Abstrakt: Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation. The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages. The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process. Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.
Competing Interests: Declaration of competing interest The authors declare that they have no competing interests.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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