watershed: Watershed transformation and watershed based object detection
In EBImage: Image processing and analysis toolbox for R

Description Usage Arguments Details Value Author(s) See Also Examples

Watershed transformation and watershed based object detection.

1	watershed(x, tolerance=1, ext=1)

`x`	An `Image` object or an array.
`tolerance`	The minimum height of the object in the units of image intensity between its highest point (seed) and the point where it contacts another object (checked for every contact pixel). If the height is smaller than the tolerance, the object will be combined with one of its neighbors, which is the highest. Tolerance should be chosen according to the range of `x`. Default value is 1, which is a reasonable value if `x` comes from `distmap`.
`ext`	Radius of the neighborhood in pixels for the detection of neighboring objects. Higher value smoothes out small objects.

The algorithm identifies and separates objects that stand out of the background (zero). It inverts the image and uses water to fill the resulting valleys (pixels with high intensity in the source image) until another object or background is met. The deepest valleys become indexed first, starting from 1.

The function bwlabel is a simpler, faster alternative to segment connected objects from binary images.

An Grayscale Image object or an array, containing the labelled version of x.

Oleg Sklyar, osklyar@ebi.ac.uk, 2007

bwlabel, propagate

  x = readImage(system.file('images', 'shapes.png', package='EBImage'))
  x = x[110:512,1:130]
  display(x, title='Binary')
  y = distmap(x)
  display(normalize(y), title='Distance map')
  w = watershed(y)
  display(normalize(w), title='Watershed')