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Morphological structuring element

A `strel`

object represents a flat morphological
*structuring element*, which is an essential part of
morphological dilation and erosion operations.

A flat structuring element is a binary valued neighborhood, either 2-D or
multidimensional, in which the true pixels are included in the morphological
computation, and the false pixels are not. The center pixel of the structuring element,
called the *origin*, identifies the pixel in the image being
processed. Use the `strel`

function (described below) to create a flat
structuring element. You can use flat structuring elements with both binary and
grayscale images. The following figure illustrates a flat structuring element.

To create a nonflat structuring element, use `offsetstrel`

.

`SE = strel('diamond',r)`

`SE = strel('disk',r,n)`

`SE = strel('line',len,deg)`

`SE = strel('octagon',r)`

`SE = strel('rectangle',mn)`

`SE = strel('square',w)`

`SE = strel('cube',w)`

`SE = strel('cuboid',xyz)`

`SE = strel('sphere',r)`

`SE = strel('arbitrary',nhood)`

`SE = strel('diamond',`

creates
a diamond-shaped structuring element, where `r`

)`r`

specifies
the distance from the structuring element origin to the points of the
diamond.

`SE = strel('disk',`

creates a disk-shaped structuring element, where `r`

,`n`

)`r`

specifies the radius. `n`

specifies the number of line
structuring elements used to approximate the disk shape. Morphological
operations using disk approximations run much faster when the structuring
element uses approximations.

`SE = strel('line',`

creates a linear structuring element that is symmetric with respect to the
neighborhood center. `len`

,`deg`

)`deg`

specifies the angle (in
degrees) of the line as measured in a counterclockwise direction from the
horizontal axis. `len`

is approximately the distance
between the centers of the structuring element members at opposite ends of
the line.

`SE = strel('octagon',`

creates
a octagonal structuring element, where `r`

)`r`

specifies the
distance from the structuring element origin to the sides of the octagon, as
measured along the horizontal and vertical axes. `r`

must
be a nonnegative multiple of 3.

`SE = strel('rectangle',`

creates a rectangular structuring element, where `mn`

)`mn`

specifies the size.

`SE = strel('cube',`

creates a
cubic structuring element whose width is `w`

)`w`

pixels.
`w`

must be a nonnegative integer scalar.

`SE = strel('cuboid',`

creates
a cuboidal structuring element of size `xyz`

)`xyz`

.

`SE = strel('arbitrary',`

creates a structuring element, where `nhood`

)`nhood`

is a matrix
of 1s and 0s that specifies the neighborhood. You can omit
`'arbitrary'`

and specify
`strel(nhood)`

.

The following syntaxes still work, but `offsetstrel`

is the preferred way
to create these nonflat structuring element shapes:

`SE = strel('arbitrary',nhood,h)`

`SE = strel('ball',r,h,n)`

The following syntaxes still work, but are not recommended for use:

`SE = strel('pair',OFFSET)`

`SE = strel('periodicline',p,v)`

Structuring elements that do not use approximations (

`n`

= 0) are not suitable for computing granulometries.

For all shapes except `'arbitrary'`

, structuring elements are
constructed using a family of techniques known collectively as *structuring
element decomposition*. The principle is that dilation by some large
structuring elements can be computed faster by dilation with a sequence of smaller
structuring elements. For example, dilation by an 11-by-11 square structuring element
can be accomplished by dilating first with a 1-by-11 structuring element and then with
an 11-by-1 structuring element. This results in a theoretical performance improvement of
a factor of 5.5, although in practice the actual performance improvement is somewhat
less. Structuring element decompositions used for the `'disk'`

shape is
an approximations—all other decompositions are exact.

[1] van den Boomgard, R, and R. van Balen, "Methods for Fast Morphological Image
Transforms Using Bitmapped Images," *Computer Vision, Graphics, and Image
Processing: Graphical Models and Image Processing*, Vol. 54, Number 3,
pp. 252–254, May 1992.

[2] Adams, R., "Radial Decomposition of Discs and Spheres," *Computer
Vision, Graphics, and Image Processing: Graphical Models and Image
Processing*, Vol. 55, Number 5, pp. 325–332, September 1993.

[3] Jones, R., and P. Soille, "Periodic lines: Definition, cascades, and
application to granulometrie," *Pattern Recognition Letters*, Vol.
17, pp. 1057–1063, 1996.

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