Watershed segmentation of an image with two regional minima:

Visualize the segmentation:

Watershed segmentation of a 3D image:

Mark two regions with a marker image:

Mark two regions with a list of positions:

Mark 3D regions and background with a binary mask:

Separate overlapping components using the distance transform image:

Visualize the separation of the overlapping components:

Use watershed segmentation to separate overlapping 3D components:

Compute distance transform of the volume:

Run watershed segmentation on the distance transform of a 3D volume and visualize the segmentation:

Use 3D markers to enhance the segmentation:

Binary image created from watershed ridges after removing the background:

Heart chamber segmentation:

Use local maxima of smoothed data as markers:

Watershed segmentation using detected marker point:

Preprocessing the image by filling shallow regional maxima helps reduce over-segmentation:

Use a combination of GradientFilter and FillingTransform to segment an image:

Approximate the Voronoi diagram of a set of points:

Compute watershed transform of the distance transform of the image:

Show the boundary of the components (Voronoi diagram) and the initial points:

Over-segmentation can be used in a creative way to finely texture the background in an image:

The methods "Basins", "Rainfall", and "MinimumSaliency" assign all pixels to a catchment basin:

The methods "Watershed" and "Immersion" compute watershed lines separating the components:

Use the default method to compute watershed lines separating the components:

Typically, plateau pixels are separated based on their proximity to regional minima:

The method "MinimumSaliency" assigns all plateau pixels to the same component:

Typically, images do not have one regional minimum per component:

Watershed segmentation is conceptually using regional minima as markers:

Computing watershed on the gradient image typically gives a more desired segmentation:

Solve a maze puzzle using watershed transform: