Huygens software is dependent on this segmentation. The SeedAndThreshold method with the garbage volume extension shows a simple but effective technique to detect objects. This method however is not efficient when object are very close to each other and blurrings of the point spread function interfere. In those cases the watershed segmentation method provides the solution to separate objects which else would merge in one object.
The watershed method build walls (watersheds) at local minima to prevent merging of objects. This results in objects containing only one local maxima. This is visualized in the images below.
If you take a look at a line-profile of an image, where the image intensities are at the y-scale and their locations are at the x-scale, the image can be seen as a topographic relief. If you invert this view, and slowly flood the relief, the watershed segmentation build watersheds there where else basins would merge into one large basin (where water start to "touch" each other).
This algorithm is implemented in the Object Analyzer and Surface Renderer and is used for the Object Tracker to automatically detect objects.
seed-level and the garbage volume keep their original functionalities, but are now applied to the split objects individually. This means that an object split by watershed segmentation may have some of their sub-objects removed.
Image segmentated with the normal SeedAndThreshold method.
Segmentation with the same seed and threshold as above, but with the watershed turned on.