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Texture Analysis

Entropy, range, and standard deviation filtering; create gray-level co-occurrence matrix

Texture analysis refers to the characterization of regions in an image by their texture content. Texture analysis attempts to quantify intuitive qualities described by terms such as rough, smooth, silky, or bumpy as a function of the spatial variation in pixel intensities. In this sense, the roughness or bumpiness refers to variations in the intensity values, or gray levels.

Texture analysis is used in various applications, including remote sensing, automated inspection, and medical image processing. Texture analysis can be used to find the texture boundaries, called texture segmentation. Texture analysis can be helpful when objects in an image are more characterized by their texture than by intensity, and traditional thresholding techniques cannot be used effectively.


entropyEntropy of grayscale image
entropyfiltLocal entropy of grayscale image
rangefiltLocal range of image
stdfiltLocal standard deviation of image
graycomatrixCreate gray-level co-occurrence matrix from image
graycopropsProperties of gray-level co-occurrence matrix


Calculate Statistical Measures of Texture

Texture analysis can classify textures by using local statistical measures such as entropy, pixel range, and pixel standard deviation.

Texture Analysis Using the Gray-Level Co-Occurrence Matrix (GLCM)

The GLCM characterizes texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships.

Create a Gray-Level Co-Occurrence Matrix

When you create a single GLCM, the default spatial relationship is defined as two horizontally adjacent pixels.

Specify Offset Used in GLCM Calculation

You can create multiple GLCMs with different spatial relationships between pixels to obtain additional information about textural features.

Derive Statistics from GLCM and Plot Correlation

This example shows how to create a set of GLCMs and derive statistics from them.

Featured Examples