Image Fuzzy Clustering Segmentation Method Based on 2D Histogram

We propose an image fuzzy clustering segmentation method based on 2D histogram analysis, which references multiple algorithm-related research papers. Our approach implements image segmentation through histogram analysis and clustering techniques, utilizing 2D spatial information to effectively extract fuzzy regions from images. The implementation involves constructing a 2D histogram that combines pixel intensity values with local spatial features, followed by applying fuzzy clustering algorithms like FCM (Fuzzy C-Means) to partition the histogram space. Key functions include histogram bin calculation, membership function optimization, and cluster validity index evaluation. Our research results demonstrate that this 2D histogram-based fuzzy clustering segmentation method shows broad application prospects in the field of image processing, particularly for handling uncertain boundaries and gradual transitions in medical imaging and remote sensing applications.