Two-Dimensional Otsu Automatic Threshold Segmentation Method for Grayscale Images

The two-dimensional Otsu automatic threshold segmentation method for grayscale images is a widely used image segmentation technique. This method automatically determines an optimal threshold by calculating the grayscale histogram of the image, effectively segmenting the image into distinct regions. In MATLAB implementation, this algorithm typically involves computing a 2D histogram that considers both pixel intensity values and local neighborhood information, creating a more robust segmentation approach compared to traditional 1D Otsu method. Key implementation steps include: calculating the co-occurrence matrix of pixel intensities and their local means, computing probability distributions, and iterating through possible threshold combinations to maximize between-class variance. The MATLAB source program utilizes matrix operations and histogram functions to efficiently process the image data, providing better understanding of image characteristics and enabling more precise analysis and processing. This method demonstrates particular effectiveness in handling noisy images and scenarios where traditional thresholding methods might fail, making it valuable for various computer vision and image processing applications.