Using a Gray-Level Co-Occurrence Matrix (GLCM). The texture filter functions provide a statistical view of texture based on the image histogram. These functions. Gray Level Co-Occurrence Matrix (Haralick et al. ) texture is a powerful image feature for image analysis. The glcm package provides a easy-to-use function. -Image Classification-. Gray Level Co-Occurrence Matrix. (GLCM) The GLCM is created from a gray-scale ▫.

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Calculating GLCM Texture

These functions can provide useful information about the texture of an image but cannot provide information about shape, i. Another statistical method that considers the spatial relationship of pixels is the gray-level co-occurrence matrix GLCMalso known as the gray-level spatial dependence matrix. The toolbox provides functions to create a GLCM and derive statistical measurements from it. The graycomatrix function creates a gray-level co-occurrence matrix GLCM by calculating how often a pixel with the intensity gray-level value i occurs in a specific spatial relationship to a pixel with the value j.

By default, the spatial relationship is defined as the pixel of interest and the pixel to its immediate right horizontally adjacentbut you can specify other spatial relationships between the two pixels. Each element i,j in the resultant glcm is simply the sum of the number of times that the pixel with value i occurred in the specified spatial relationship to a pixel with value j in the input image.

Because the processing required to calculate a GLCM for the full dynamic range of an image is prohibitive, graycomatrix scales the input image. By default, graycomatrix uses scaling to reduce the number of intensity values in grayscale image from to eight. The number of gray levels determines the size of the GLCM.

To control the number of gray levels in the GLCM and the scaling of intensity values, using the NumLevels and the GrayLimits parameters of the graycomatrix function. See the graycomatrix tutoriaal page titorial more information. The gray-level co-occurrence matrix can reveal certain properties about the spatial distribution of the gray levels in the texture image. For example, if most of the entries in the GLCM are concentrated along the diagonal, the texture is coarse with respect to the specified offset.


GLCM Texture: A Tutorial v. March

You can also derive several statistical measures from the GLCM. To illustrate, the following figure shows how graycomatrix calculates the first three values in a GLCM.

In the output GLCM, element 1,1 contains the value 1 because there is only one instance in the input image where two horizontally adjacent pixels have the values 1 and 1 tutoriaal, respectively. Element 1,3 in the GLCM has the value 0 because there are no instances of two horizontally adjacent pixels with the values 1 and 3. Specifying the Offsets By default, the graycomatrix function tutodial a single GLCM, with the spatial relationship, or offsetdefined as two horizontally adjacent pixels.

The GLCM Tutorial Home Page | Personal and research

However, a single GLCM might not be enough to describe the textural features of the input image. For example, a single horizontal offset might not be sensitive to texture with a vertical orientation. For this reason, graycomatrix can create multiple GLCMs for a single input image. To create multiple GLCMs, specify an array of offsets to the graycomatrix function.

These offsets define pixel relationships of varying direction and distance.

For example, you can define an array of offsets that specify four directions horizontal, vertical, and two diagonals and four distances. In this case, the input image is represented by 16 GLCMs.

When you calculate statistics from these GLCMs, you can take the average. You specify these offsets as a p -by-2 array of integers. This example creates an offset that specifies four directions and 4 distances for each direction.


For more information about specifying offsets, see the graycomatrix reference page. These statistics provide information about the texture of an image. The following table lists the statistics you can derive. You specify the statistics you want when you call the graycoprops function. For detailed information about these statistics, see the graycoprops reference page.

The GLCM Tutorial Home Page

Statistic Description Contrast Measures the local variations in the gray-level co-occurrence matrix. Correlation Measures the joint probability occurrence of the specified pixel pairs.

Also known as uniformity or the angular second moment. Plotting the Correlation This example shows how to create a set of GLCMs and derive statistics from them and illustrates how the statistics returned by graycoprops have a direct relationship to the original input image.

Provides the sum of squared elements in the GLCM. Read in a grayscale image and display it.

Because the image contains tktorial of a variety of shapes and sizes that are arranged in horizontal and vertical directions, the example specifies a set of horizontal offsets that only vary in distance. Call the graycomatrix function specifying the offsets.

The example calculates the contrast and correlation. Correlation] ; tugorial ‘Texture Correlation as a tutkrial of offset’ ; xlabel ‘Horizontal Offset’ ylabel ‘Correlation’ The plot contains peaks at offsets 7, 15, 23, and If you examine the input image closely, you can see that certain vertical elements in the image have a periodic pattern that repeats every seven pixels. The following figure shows the upper left corner of the image and points out where this pattern occurs.

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