Morphological Gradient for Binary Image Edge Detection and Watershed Thresholding for Image Segmentation

In this context, MATLAB-coded programs play a crucial role. These implementations include functionality for detecting edges in binary images using morphological gradient operations and segmenting images through watershed thresholding techniques. The morphological gradient approach enhances edge detection accuracy by calculating the difference between dilated and eroded versions of the image, typically implemented using MATLAB's imdilate and imerode functions with structural elements. The watershed algorithm, often combined with gradient magnitude calculations, effectively separates overlapping objects by treating the image as a topographic surface. Through these programmatic implementations, we achieve more precise image edge detection and perform segmentation based on specific threshold values. These capabilities significantly improve outcomes in image processing applications while providing greater processing flexibility through parameters such as structuring element size, threshold levels, and marker-controlled watershed modifications.