Implementing Optimal Automatic Image Threshold Segmentation Using MATLAB Source Code

Implementing optimal automatic image threshold segmentation using MATLAB source code is a widely adopted technique in image processing. We have selected the renowned Otsu's method as our core algorithm, which calculates the optimal threshold value by maximizing the inter-class variance between background and foreground pixels. This algorithm is particularly effective for bimodal histograms and requires computing probability distributions of pixel intensities. The implementation involves several key MATLAB functions: reading images using imread(), converting to grayscale with rgb2gray(), calculating the optimal threshold through Otsu's statistical analysis, and applying the threshold using logical operations or the imbinarize() function. Through this algorithm, we can effectively partition images into distinct regions, facilitating subsequent analysis and processing tasks. Automatic threshold segmentation represents a crucial technique in image processing workflows, enabling rapid and accurate extraction of target regions from images for further manipulation. In our project, by implementing optimal automatic threshold segmentation using MATLAB source code, we achieve enhanced capability to meet specific requirements while improving the efficiency of image data processing and analysis. The Otsu method implementation typically involves histogram calculation, probability distribution analysis, and iterative threshold evaluation to determine the optimal separation point.