ranking image segmentation methods using data envelopment analysis

This study leverages a model based on data envelopment analysis (dea) to rank various image segmentation methods, identifying the most efficient approach for specific scenarios. by employing two case studies, we illustrate the application and effectiveness of dea in comparing computational efficiency and segmentation accuracy across methods. the chan-vese method demonstrated notable performance, particularly at the regularization parameter μ=0.01, where it achieved optimal efficiency in three cases. similarly, the bernard method, utilizing variational approaches with b-splines for contour representation, excelled at the smoothing parameter h=3, achieving the highest efficiency in three cases as well. this research emphasizes the critical role of selecting suitable segmentation algorithms tailored to image characteristics, computational demands, and accuracy requirements. by incorporating metrics such as average computation times and mean sum of square distance (mssd) accuracy, the study provides an objective framework for performance comparison. results indicate significant variations in method efficiency based on input parameters, underscoring the importance of adaptive parameter tuning for achieving optimal outcomes. the findings contribute to advancing the evaluation and optimization of segmentation techniques, offering valuable insights for applications in medical imaging, geoinformatics, and beyond