MATLAB Image Binarization: Methods and Implementation

Image binarization serves as a crucial method in image segmentation. During the binarization process, pixels with intensity values exceeding a critical threshold are assigned the maximum grayscale value, while those below the threshold receive the minimum value, thereby creating a binary image. Binarization algorithms are primarily classified into fixed threshold and adaptive threshold methods based on their threshold selection mechanisms. Among the most widely used binarization techniques are: - Bimodal method: Suitable for images with distinct foreground and background peaks in their histogram - P-parameter method: Determines threshold based on predefined probability distributions - Iterative method: Progressively refines threshold through convergence algorithms - OTSU method: Automatically calculates optimal threshold by maximizing inter-class variance In MATLAB implementation, these methods can be implemented using functions like graythresh() for OTSU's method or adaptthresh() for adaptive thresholding. The choice of method depends on image characteristics and specific application requirements to achieve optimal segmentation results. For custom implementations, developers can utilize histogram analysis and threshold calculation algorithms while considering image preprocessing steps for improved performance.