MATLAB Image Segmentation Code Based on Grayscale Thresholding

I have developed a MATLAB-based image segmentation code that performs partitioning based on grayscale intensity values. The implementation utilizes thresholding techniques where pixel intensities are compared against dynamic or fixed thresholds to separate regions of interest. The code offers significant flexibility through configurable parameters including threshold values, morphological operations, and region-growing criteria that can be adapted for specific requirements. This segmentation approach is particularly useful for computer vision applications such as object detection, image recognition, and analytical processing. The algorithm works by first converting color images to grayscale (if necessary) using weighted channel combination, then applying intensity histogram analysis to determine optimal segmentation thresholds. Key functions include graythresh for automated threshold calculation and bwconncomp for connected component analysis. Through this implementation, users can gain deeper insights into image characteristics and structural patterns, thereby improving processing accuracy and computational efficiency. The modular architecture ensures excellent extensibility, allowing straightforward integration of additional functionalities like edge detection, watershed algorithms, or machine learning-based segmentation methods. Overall, this grayscale-based segmentation code serves as a robust and adaptable tool that enables researchers and engineers to achieve superior results in digital image processing applications. The code structure emphasizes clear documentation and parameter注释 to facilitate easy modification and enhancement.