Image Threshold Calculation Using Iterative Method with MATLAB Implementation

In the MATLAB environment, the iterative method can be employed to calculate optimal image thresholds for subsequent binarization processing. The iterative method represents a fundamental numerical computation approach that achieves solutions through progressive approximation. The algorithm typically follows these steps: initial threshold estimation, pixel classification into foreground/background groups, mean intensity calculation for each group, and threshold updating using the average of both means. This process iterates until convergence criteria are met (e.g., threshold change below tolerance level). MATLAB implementation involves key functions such as imread() for image loading, graythresh() for Otsu's method (alternative approach), and imbinarize() for final binarization. The iterative thresholding algorithm can be programmed using while loops with conditional statements comparing current and previous threshold values. This method allows gradual threshold optimization to achieve optimal binarization effects, enabling improved image processing outcomes for specific application requirements. The implementation typically includes histogram analysis for initial threshold selection and convergence monitoring for algorithm efficiency.