Maximum Fuzzy Entropy Threshold Image Segmentation Using 2D Histogram

This article presents a maximum fuzzy entropy threshold image segmentation method based on 2D histogram analysis. Compared to traditional 1D maximum fuzzy entropy approaches, this technique demonstrates significantly improved segmentation performance. The implementation utilizes an S-function as the membership function to handle the inherent fuzziness in image boundaries and intensity distributions. From a computational perspective, the algorithm involves constructing a 2D histogram representing the joint distribution of pixel intensities and their local neighborhood averages, then iteratively searching for the optimal threshold that maximizes the fuzzy entropy measure. This approach not only yields more accurate segmentation results but also preserves finer image details by considering spatial context information. The S-function membership model, typically implemented using sigmoid functions in code, effectively handles the transition regions between different segments. Therefore, this 2D histogram-based maximum fuzzy entropy threshold segmentation method represents an efficient and robust technique for advanced image processing applications.