Adaptive Image Segmentation Using Gray Otsu Threshold Method

In the field of adaptive image segmentation, the Gray Otsu threshold method provides an effective approach for automatic image segmentation. This technique leverages the Otsu algorithm by calculating the gray histogram of an image to determine the optimal threshold value, effectively partitioning the image into two or more distinct regions. Key implementation aspects in MATLAB include: - Using the graythresh() function to automatically compute the optimal threshold based on image histogram analysis - Applying the imbinarize() function with the Otsu threshold to convert grayscale images to binary format - The algorithm works by maximizing the between-class variance while minimizing within-class variance This method enables more precise identification and analysis of different image regions, providing a solid foundation for subsequent image processing and analytical tasks. The MATLAB implementation allows for efficient processing of various image types while maintaining segmentation accuracy through adaptive threshold determination.