Algorithmic Implementation and Evaluation for Image Segmentation Techniques
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 1
Abstract
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 diverse set 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 outperformed its counterparts, achieving perfect scores in Jaccard Index, Dice Coefficient, Pixel Accuracy, and Mean Intersection over Union, while minimizing Hausdorff Distance to 0. This emphasized its 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 application requirements 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, making this study a valuable resource for informed decision-making in diverse imaging applications.
Authors and Affiliations
Umer Ijaz, Fouzia Gillani, Muhammad Saad Sharif, Ali Iqbal, Muhammad Fraz Anwar, Abubaker Ijaz
Keywords
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