Multiple Threshold-Based Image Segmentation Methods with MATLAB Implementation

This MATLAB implementation showcases multiple threshold-based image segmentation methods, featuring Otsu's method for automatic threshold determination using inter-class variance maximization, maximum entropy thresholding that optimizes information preservation, and clustering-based techniques for pixel classification. These algorithms enable effective image processing by extracting regions of interest through optimal threshold selection. The implementation demonstrates how to achieve accurate segmentation results using MATLAB's image processing toolbox functions like graythresh for Otsu's method and custom entropy calculations for threshold optimization. By providing multiple approaches, this collection offers flexibility in choosing appropriate segmentation strategies based on specific application requirements, ultimately enhancing efficiency and precision in digital image processing workflows. The code includes detailed comments explaining each algorithm's mathematical foundation and practical implementation considerations.