LIFWCM: local information-based fuzzy weighted C-means algorithm for image segmentation

Image segmentation aims to partition an image into non-overlapping regions that are coherent in appearance. Although the fuzzy C-means (FCM) algorithm is widely used for its simplicity and efficiency, it treats each pixel independently and is therefore sensitive to noise. We propose LIFWCM, a local information-based fuzzy weighted C-means algorithm that assigns a single-pass, data-driven weight to each pixel by aggregating neighborhood intensity variation and positional overlap, and then integrates these weights into the standard FCM objective and a spatially aware membership refinement. This design suppresses the influence of noisy and boundary pixels while preserving details with low computational overhead. Across six experiments on synthetic images and natural images from the Image Processing Toolbox and BSDS500, LIFWCM consistently improves segmentation quality under heavy noise. On the BSDS500 image with 30% salt-and-pepper noise, LIFWCM attains 98.96% segmentation accuracy, exceeding the best baseline, and surpassing classical FCM variants. LIFWCM also achieves higher MPA (0.94) and MIoU (0.82) than competing methods, while converging in a few iterations. These results demonstrate that LIFWCM is robust to high-intensity noise, preserves fine structures, and remains efficient due to one-time weight computation, making it suitable for real-world noisy images with complex structures.