Image Segmentation Using Fuzzy Logic Development

This document introduces an image segmentation program developed using fuzzy logic principles. The implementation incorporates fuzzy clustering algorithms (such as FCM - Fuzzy C-Means) for pixel classification, where membership functions define the degree to which each pixel belongs to different image regions. The program not only performs advanced image classification but also handles preprocessing and postprocessing of image data through fuzzy inference systems, providing users with flexible functionality and configurable options. By leveraging fuzzy logic's ability to handle uncertainty and imprecision, our segmentation approach achieves higher accuracy in boundary detection and region separation, making image processing more precise and computationally efficient. Key functions include adaptive thresholding via fuzzy rules and multi-criteria decision-making for heterogeneous texture analysis. This program finds applications across multiple domains including medical imaging (e.g., tumor boundary delineation), computer vision systems, and industrial image analysis. Whether performing complex image classification, large-scale data processing, or specialized operations, our fuzzy logic-based segmentation solution meets diverse requirements while delivering optimized results through parameter-tunable membership functions and rule bases.