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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This example creates an offset that specifies four directions and 4 distances for each direction.

Calculating GLCM Texture | r Tutorial

However the author is not an expert in these fields glccm texture’s use there is not covered in detail. When citing, please give the current version and its date. Also useful for researchers undertaking the use of texture in classification and other image analysis fields.

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. 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.

Statistic Description Tutoial Measures the local variations in the gray-level co-occurrence matrix.

Calculating GLCM Texture

The gray-level co-occurrence matrix can reveal certain properties about the spatial distribution of the gray levels in the texture image. By default, graycomatrix uses scaling to reduce the number of intensity values in grayscale image from to eight. 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.


Provides the sum of squared elements in the GLCM. Some features of this site may not work without it.

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. You specify these offsets as a p -by-2 array of integers. When you are done, click the answer link to see the answer and calculations.

Because the processing required to calculate a GLCM for the full dynamic range of an image is prohibitive, graycomatrix scales the input image. In addition, many users have discovered computational errors and thtorial out tugorial of improvement that gcm gone into subsequent versions of the tutorial in a Wiki-like process without the software.

When you calculate statistics from these GLCMs, you can take the average. For this reason, graycomatrix can create multiple GLCMs for a single tuforial image. Although this tutorial is not published by a professional journal, it has undergone extensive peer review by third-party reviewers at the request of the author.

To create multiple GLCMs, specify an array of offsets to the graycomatrix function. The essence is understanding the calculations and how to do them.

GLCM Texture: A Tutorial v. March

By default, the spatial relationship is defined as the pixel of interest tutkrial the pixel to its immediate right horizontally adjacentbut you can specify other spatial relationships between the two pixels. Except where otherwise noted, this item’s license is described as Attribution Non-Commercial 4.

The “NEXT” button at the bottom of the page takes you through tutoriao tutorial in sequence. The toolbox provides functions to create a GLCM and derive statistical measurements from it.


These statistics provide information about the texture of an image. Metadata Show full item record. Call the graycomatrix function specifying the offsets. For example, a single horizontal offset might not be sensitive to texture with a vertical orientation. The following figure shows the upper left corner of the image and points out where this pattern occurs.

To illustrate, the following figure shows how graycomatrix calculates the first three values in a GLCM. For more information about specifying offsets, see the graycomatrix reference page. Correlation] ; title ‘Texture Correlation as a function of offset’ ; xlabel ‘Horizontal Offset’ ylabel ‘Correlation’ The plot contains peaks at offsets 7, 15, 23, and Click on a link below to connect directly with the main sections in this tutorial.

University of Calgary University Dr. To many image analysts, they are a button you push in the software that yields a band whose use improves classification – or not.

The GLCM Tutorial Home Page

Please e-mail any broken links, comments or corrections to mhallbey glfm. May be of use for algorithm and app developers serving these communities. Some information is provided to make the material accessible to specialists in fields other than remote sensing, for example medical imaging and industrial quality control.

There are exercises to perform. For example, you can define an array of offsets that specify four directions horizontal, vertical, and two diagonals and four distances.