One-Dimensional Maximum Entropy Method for Grayscale Image Threshold Segmentation

This MATLAB source code implements the one-dimensional maximum entropy method for grayscale image threshold segmentation. The algorithm helps achieve superior segmentation of grayscale images by extracting target objects effectively through optimal threshold selection based on entropy maximization. The implementation includes key functions for calculating image histograms, computing probability distributions, and iterating through possible thresholds to find the value that maximizes the entropy criterion. The source code can process various types of images and allows for customization and optimization according to specific requirements. The modular design ensures clear understanding of the algorithm flow, including the entropy calculation function that processes pixel intensity distributions and the threshold optimization loop that evaluates all possible segmentation points. Users can easily modify parameters and extend functionality for improved results, such as adjusting the intensity range or incorporating additional segmentation criteria. The code structure is straightforward with well-commented sections covering histogram computation, probability normalization, entropy calculation for foreground and background regions, and threshold decision making. This makes it convenient for both educational purposes and practical image processing applications.