KSW Thresholding, Minimum Skewness Method, and Information Entropy Iterative Method with MATLAB Code Implementation

The MATLAB m-files implementing KSW thresholding, minimum skewness method, and information entropy iterative method were custom-developed during my research project for threshold segmentation applications. These algorithms employ distinct statistical approaches: KSW method maximizes between-class variance using Otsu's algorithm principle, minimum skewness method minimizes the asymmetry coefficient of histogram distributions, and information entropy iterative method optimizes threshold selection based on entropy maximization criteria. These methods enabled more accurate data processing and analysis by automatically determining optimal segmentation thresholds, significantly improving result reliability. The implementation includes histogram preprocessing, iterative threshold optimization loops, and performance validation modules. I selected these techniques based on their proven effectiveness in prior research and compatibility with my specific research domain requirements. Through employing these algorithms with appropriate parameter tuning and boundary condition handling, I achieved enhanced interpretation of research findings and derived meaningful conclusions. The code architecture also facilitated deeper investigation of segmentation phenomena and provides valuable reference framework for future research. In summary, these threshold segmentation methodologies played a crucial role in the success of my research project by delivering robust, automated image partitioning capabilities.