Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Create gray-level co-occurrence matrix from image

`glcms = graycomatrix(I)`

`glcms = graycomatrix(I,Name,Value,...)`

```
[glcms,SI]
= graycomatrix(___)
```

creates
a gray-level co-occurrence matrix (GLCM) from image `glcms`

= graycomatrix(`I`

)`I`

.
Another name for a gray-level co-occurrence matrix is a *gray-level
spatial dependence matrix*. Also, the word co-occurrence
is frequently used in the literature without a hyphen, cooccurrence.

`graycomatrix`

creates the GLCM by calculating
how often a pixel with gray-level (grayscale intensity) value *i* occurs
horizontally adjacent to a pixel with the value *j*.
(You can specify other pixel spatial relationships using the `'Offsets'`

parameter.)
Each element (*i,j*) in `glcm`

specifies
the number of times that the pixel with value *i* occurred
horizontally adjacent to a pixel with value *j*.

returns
one or more gray-level co-occurrence matrices, depending on the values
of the optional name/value pairs. Parameter names can be abbreviated,
and case does not matter. `glcms`

= graycomatrix(I,`Name,Value`

,...)

`graycomatrix`

calculates the GLCM from a scaled
version of the image. By default, if `I`

is a binary
image, `graycomatrix`

scales the image to two gray-levels.
If `I`

is an intensity image, `graycomatrix`

scales
the image to eight gray-levels. You can specify the number of gray-levels `graycomatrix`

uses
to scale the image by using the `'NumLevels'`

parameter,
and the way that `graycomatrix`

scales the values
using the `'GrayLimits'`

parameter .

The following figure shows how `graycomatrix`

calculates
several values in the GLCM of the 4-by-5 image `I`

.
Element (1,1) in the GLCM contains the value `1`

because
there is only one instance in the image where two, horizontally adjacent
pixels have the values `1`

and `1`

.
Element `(1,2)`

in the GLCM contains the value `2`

because
there are two instances in the image where two, horizontally adjacent
pixels have the values `1`

and `2`

. `graycomatrix`

continues
this processing to fill in all the values in the GLCM.

`graycomatrix`

ignores pixel pairs if either
of the pixels contains a `NaN`

, replaces positive `Infs`

with
the value `NumLevels`

, and replaces negative `Infs`

with
the value `1`

. `graycomatrix`

ignores
border pixels, if the corresponding neighbor pixel falls outside the
image boundaries.

The GLCM created when `'Symmetric'`

is set
to `true`

is symmetric across its diagonal, and is
equivalent to the GLCM described by Haralick (1973). The GLCM produced
by the following syntax, with `'Symmetric'`

set to `true`

graycomatrix(I, 'offset', [0 1], 'Symmetric', true)

is equivalent to the sum of the two GLCMs produced by the following
statements where`'Symmetric'`

is set to `false`

.

graycomatrix(I, 'offset', [0 1], 'Symmetric', false) graycomatrix(I, 'offset', [0 -1], 'Symmetric', false)

[1] Haralick, R.M., K. Shanmugan, and I. Dinstein, "Textural Features for Image Classification", IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, 1973, pp. 610-621.

[2] Haralick, R.M., and L.G. Shapiro. Computer and Robot Vision: Vol. 1, Addison-Wesley, 1992, p. 459.

Was this topic helpful?