Marker-Based Watershed Segmentation Algorithm

In this article, we explore the implementation of the marker-based watershed segmentation algorithm to handle scenarios where target objects are connected within an image. The watershed algorithm is a powerful image segmentation technique that partitions an image into two or more distinct regions. The core concept treats the image as a topographic map where elevation peaks represent target objects and valleys correspond to background regions. Through strategic placement of markers to identify objects and background areas, the watershed algorithm can effectively separate these regions using gradient magnitude calculations and flooding simulations.

When dealing with interconnected objects, the standard watershed algorithm may encounter oversegmentation issues. To overcome this challenge, implementation strategies include applying morphological operations like opening and closing to refine segmentation results, or integrating complementary segmentation algorithms to enhance marker placement. These techniques typically involve using distance transforms and connected component analysis to improve segmentation accuracy. By combining these approaches with careful parameter tuning in functions like cv2.watershed() from OpenCV or watershed() in MATLAB's Image Processing Toolbox, we can achieve more precise separation of connected objects and obtain optimized segmentation outcomes.