MATLAB Implementation of Binarization Algorithms with Code Examples

This article explores a fundamental algorithm in image processing: binarization algorithms. These algorithms convert pixel values in images into binary form to facilitate subsequent processing. We will examine several distinct binarization methods, including Otsu's method, Niblack's technique, Kapur's entropy-based approach, and the Kittler-Met algorithm. Each algorithm possesses unique characteristics and application scenarios, warranting detailed analysis and comparison to enhance understanding and practical implementation. From a code implementation perspective, Otsu's method automatically determines the optimal threshold by maximizing inter-class variance, achievable in MATLAB using graythresh() function combined with imbinarize(). Niblack's algorithm adapts thresholds locally using mean and standard deviation within a sliding window, requiring neighborhood operations that can be implemented with nlfilter() or blockproc() functions. Kapur's method employs entropy maximization for threshold selection, involving probability distribution calculations of image histograms. The Kittler-Met algorithm minimizes classification error using Gaussian mixture models, which can be implemented through iterative optimization techniques. We will provide MATLAB code snippets demonstrating key implementation steps for each algorithm, discuss their computational complexity, and compare their performance under different image conditions such as varying lighting conditions, noise levels, and texture complexities. This comprehensive analysis will equip readers with practical knowledge for selecting and implementing appropriate binarization techniques for specific image processing tasks.