Image Segmentation Using Otsu's Method and Iterative Approach

This article presents an image segmentation methodology utilizing Otsu's method combined with an iterative approach. This technique finds extensive application in digital image processing as it enables automatic target extraction from images without requiring substantial prior knowledge. The implementation workflow typically involves: Otsu's algorithm automatically calculating an optimal threshold by maximizing inter-class variance, which converts the grayscale image into a binary format. The iterative method then refines this initial segmentation through cyclic optimization processes that enhance both accuracy and stability. The algorithm may involve functions like cv2.threshold() with OTSU flag for initial binarization, followed by iterative morphological operations or region-growing techniques. Compared to conventional methods, this combined approach consistently yields more precise and clearer segmentation results, making it highly valuable for practical applications with promising implementation prospects across various computer vision domains.