Akademik Veri Yönetim Sistemi
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, cilt.28, sa.6, 2025 (SCI-Expanded)
Multi-level thresholding image segmentation (MTIS) becomes a difficult and complex problem as the number of thresholds increases. Therefore, meta-heuristic algorithms (MHS) are generally used to solve MTIS problems. However, many problems are encountered in MHS-based MTIS applications. Optimization studies are carried out using different parameter settings and competing algorithms arbitrarily determined by researchers. A few algorithms were used in the experiments, and the optimum solutions were not investigated sufficiently. Also, the feasible solutions were not investigated, and the stability and computational complexity of the algorithms were not analyzed in depth. To solve these problems, Two-Stage Multilevel Image Segmentation (TSMIS) approach was introduced in this study. In the first phase, competitive algorithms, optimum and feasible solutions were determined for the segmentation problems. In the second phase, algorithms that exhibit competitive convergence performance in finding feasible solutions were investigated and their stability analysis was performed. Thanks to TSMIS, an experimental study procedure was developed that defines minimum search conditions to find optimal and feasible solutions. Standards were defined to ensure fairness among competing algorithms and to identify competitive algorithms. An approach was introduced to analyze the stability of algorithms and reveal their computational complexity. In this study, fifteen images from the USC-SIPI image database and Berkeley Segmentation Dataset, two thresholding functions, ten different number of thresholds, and sixty-eight MHS algorithms were used to test and validate the proposed method. According to the statistical analysis results, 13 of the 68 competing algorithms were found to be competitive. 6 of these competitive algorithms- Path Finder (PF), Yin-Yang-Pair Optimization, Linear Population Size Reduction Adaptive Differential Evolution, Fitness-Distance-Balance Based Manta-Ray Foraging Optimization, Supply-Demand-Based Optimization, and Atom Search Algorithm- were applied for the first time to MTIS problem in this study. The stability and computational complexity of the algorithms were also analyzed for the first time in the study. The proposed approach is a candidate to provide reusable data for the execution of future image segmentation studies and to be a standard approach for meta-heuristic-based MTIS. According to the findings, it was concluded that the minimum value of the maxFEs parameter has changed for different MTIS problems, and the minimum value should be maxFEs = 3000 * number of thresholds.