Image Segmentation Using Watershed Threshold Method

The watershed threshold method is an image segmentation technique based on morphological operations, primarily used for processing grayscale images with complex backgrounds or overlapping objects. This approach combines mathematical morphology with top-hat and bottom-hat transformations, ultimately employing the watershed algorithm for precise segmentation.

Initially, the top-hat transformation extracts bright features from the image, while the bottom-hat transformation captures dark features. By computing the difference between these two transformations, local contrast in the image is effectively enhanced, making the distinction between objects and background more pronounced. In code implementation, this typically involves using morphological opening (top-hat) and closing (bottom-hat) operations with appropriate structuring elements.

During the preprocessing stage, this enhancement operation helps eliminate interference caused by uneven illumination while emphasizing edges and details in target regions. Subsequently, the watershed algorithm is applied to segment the enhanced image. The watershed algorithm treats the image as a topographic map based on gradient information, simulating a flooding process to ultimately divide the image into multiple distinct, independent regions. Key implementation steps include gradient computation, marker extraction, and watershed transformation using functions like cv2.watershed() in OpenCV.

This method is particularly suitable for scenarios such as medical imaging and material microscopy images, where objects may be difficult to segment due to illumination variations or structural overlaps. Through the watershed threshold method, segmentation accuracy can be improved, reducing issues of over-segmentation or under-segmentation. Proper parameter tuning of morphological operations and marker selection is crucial for optimal results.