Algorithmic Implementation and Evaluation for Image Segmentation Techniques

This research conducts a comprehensive comparative analysis of five prominent image segmentation algorithms, including Thresholding, K-Means Clustering, Mean Shift, Graph-Based Segmentation (Watershed), and U-Net (Deep Learning). The study employs a diverseset of five images and associated masks to rigorously evaluate algorithmic performance using key metrics such as Jaccard Index, Dice Coefficient, Pixel Accuracy, Hausdorff Distance, and Mean Intersection over Union. The findings reveal that the Threshold Algorithm consistently outperformedits counterparts, achieving perfect scores in Jaccard Index, Dice Coefficient, Pixel Accuracy, and Mean Intersection over Union, while minimizing Hausdorff Distance to 0. This emphasizedits exceptional accuracy, precision, and agreement with ground truth segmentation, positioning it as an optimal choice for applications demanding precise segmentation, such as medical imaging or object detection. The research underscores the need to carefully consider specific applicationrequirements and tradeoffs when selecting an algorithm, offering valuable guidance to researchers and practitioners in the field of image segmentation. The standardized approach outlined in the material and methods section ensures fair comparisons, makingthis study a valuable resource for informed decision-making in diverse imaging applications.