Adaptive Threshold Segmentation for Grayscale Images

When performing adaptive threshold segmentation on grayscale images using MATLAB, the following implementation steps can be adopted. First, we need to load the image and convert it to grayscale format using functions like imread() and rgb2gray(). Then, we can utilize MATLAB's adaptive threshold segmentation algorithm, primarily implemented through the adaptthresh() function, which calculates threshold values based on local image regions. This algorithm employs statistical methods (such as local mean or Gaussian-weighted sum) to determine optimal thresholds for each pixel, effectively handling uneven illumination distributions across the image. The adaptthresh() function typically requires parameters like neighborhood size and sensitivity factor, allowing customization for different image characteristics. After obtaining the adaptive threshold, we apply it using imbinarize() to create a binary segmentation mask. The segmented image can then be used for subsequent analysis or processing tasks, such as object detection using regionprops() or edge detection with edge() functions. By leveraging MATLAB's adaptive threshold segmentation capabilities, we can more effectively extract target information from images and perform more accurate image analysis and processing. The key advantages include better preservation of detail in varying lighting conditions and improved handling of background complexity compared to global thresholding methods.