Self-Developed Watershed Algorithm Implementation for Image Segmentation

This article presents my custom implementation of the watershed algorithm, designed to facilitate image segmentation tasks. The watershed algorithm is a widely-used segmentation technique that operates by treating image intensity values as topographic surfaces and identifying catchment basins. My program automates the watershed transformation process through several key stages: first, it converts input images to grayscale and applies gradient magnitude calculation using Sobel or similar operators. The implementation includes marker-controlled watershed processing to prevent over-segmentation, where foreground and background markers are automatically identified through morphological operations. The core algorithm utilizes queue-based flooding simulation to partition the image into distinct regions. This implementation handles boundary detection and region merging through customizable threshold parameters. I've incorporated error handling for invalid inputs and optimized memory management for large image processing. This program aims to assist professionals and researchers working with image segmentation in their daily tasks. For any technical questions or improvement suggestions regarding the algorithm implementation, please feel free to contact me.