Implementing a Local Thresholding Algorithm for Images in MATLAB

This article explores how to implement a local thresholding algorithm for images using MATLAB. We will demonstrate the step-by-step implementation process and provide MATLAB code examples to achieve effective local thresholding. The implementation involves key image processing techniques such as image segmentation and binarization, where we'll utilize MATLAB's Image Processing Toolbox functions like blockproc for processing image regions locally and adaptthresh for adaptive threshold calculations.

We'll examine the algorithm's core components, including neighborhood analysis for calculating local thresholds and the sliding window approach for pixel-wise processing. The discussion covers practical applications of local thresholding in various domains and explains how this method enhances image processing outcomes compared to global thresholding techniques, particularly for images with uneven illumination or varying contrast.

Through this tutorial, readers will gain deeper insights into image processing fundamentals and acquire practical skills to solve real-world problems using MATLAB's computational capabilities for localized image analysis and enhancement.