Five Global Thresholding Methods for Pixel-Based Image Segmentation Implementation

There are five primary global thresholding methods for pixel-based image segmentation. The first is the Minimum Extremum Method, which identifies the local minima in the image histogram as the segmentation threshold - implementation typically involves scanning histogram valleys using peak detection algorithms. The second approach, Optimal Threshold Method, analyzes the grayscale histogram to select the threshold that best separates foreground and background classes, often utilizing probability distribution modeling. Third is the Maximum Variance Method (Otsu's Method), which determines the threshold by maximizing inter-class variance between foreground and background regions through exhaustive search across all possible thresholds. Next, the Maximum Entropy Method establishes the optimal threshold by maximizing the entropy of the histogram distribution, commonly implemented using probability-based entropy calculations. Finally, the Iterative Method refines the threshold through successive approximations, where each iteration recalculates the threshold based on current segmentation results until convergence criteria are met.