Akademik Veri Yönetim Sistemi
Evolving Systems, cilt.16, sa.1, 2025 (SCI-Expanded)
Image segmentation is a pivotal phase in the image processing pipeline, offering detailed insights into various image features. Traditional segmentation methods grapple with challenges such as local minima and premature convergence when navigating intricate pixel search spaces. Additionally, these algorithms experience prolonged processing times as the number of threshold levels increases. To mitigate these issues, we implemented the Chaotic Gravitational Search Algorithm (CGSA), a robust optimizer, for the multi-level thresholding of COVID-19 chest CT scan images. CGSA amalgamates the Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation. Kapur’s entropy method was employed to partition the sample images based on optimal pixel values. The segmentation performance of CGSA was rigorously assessed on various COVID-19 chest CT scan imaging datasets from Kaggle, utilizing metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). The qualitative analysis encompassed convergence curves, segmented graphs, colormap images, histogram curves, and boxplots. Statistical validation was conducted using the signed Wilcoxon rank sum test, and eight sophisticated heuristic algorithms were enlisted for comparative analysis. The comprehensive evaluation unequivocally demonstrated CGSA's superiority in terms of processing time efficiency and its ability to provide optimal values for image quality metrics, establishing it as a powerful tool for quickly assessing COVID-19 disease severity.