Image Segmentation Using Otsu's Method

In this experiment, we will demonstrate how to use MATLAB's implementation of Otsu's method for image segmentation to better understand the fundamentals of digital image processing. Otsu's method is a popular threshold-based segmentation algorithm that automatically determines an optimal threshold value to separate an image into two regions: background and foreground. We will utilize the classic Lena test image for demonstration and experimentation, observing how different parameters and threshold values affect the segmentation results. The implementation typically involves calculating the image histogram and iterating through possible threshold values to maximize between-class variance using MATLAB's graythresh() function, which returns the optimal threshold value that can then be applied with imbinarize(). We will also explore related image processing concepts and techniques such as grayscale transformation, image binarization, and edge detection. Through this hands-on experiment, you will gain deeper insights into fundamental digital image processing concepts and techniques, enabling further exploration and application of these methods in practical scenarios.