Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183562,
        author = {vinay kumar ks and Dr.G shashibhushan},
        title = {Comparative Analysis of Image Segmentation Techniques for OCT Imaging: Evaluating Performance Using MSE and PSNR Metrics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2433-2444},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183562},
        abstract = {Image segmentation is an essential step in picture processing, involving the division of a photo into smaller segments for extra efficient evaluation. Various strategies, such as thresholding, Edge detection, clustering, graph cut segmentation, watershed segmentation, and flood fill, had been explored for this reason. Among these, clustering-based totally segmentation has established especially effective for separating images in OCT pictures. Additionally, preprocessing methods just like the Wiener filter out are hired to reduce speckle noise. Evaluation of segmentation strategies includes calculating metrics like MSE (Mean square Error) and PSNR (Peak signal-to-noise ratio) to assess segmented photo pleasant. Techniques like thresholding, edge detection (utilizing algorithms consisting of Sobel, Canny, Prewitt and Robert's), clustering, graph reduce segmentation, Watershed segmentation and flood fill are compared primarily based on these metrics. This article evaluations these segmentation strategies and their overall performance, emphasizing the importance of choosing the right technique primarily based at the particular necessities of the photo processing venture.},
        keywords = {Clustering; Edge detection; Image segmentation; Region-based; Threshold, graph cut segmentation and flood fill.},
        month = {August},
        }