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 a variety of 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.

The toolbox includes several texture analysis functions that filter an image using standard statistical measures. These statistics can characterize the texture of an image because they provide information about the local variability of the intensity values of pixels in an image. For example, in areas with smooth texture, the range of values in the neighborhood around a pixel will be a small value; in areas of rough texture, the range will be larger. Similarly, calculating the standard deviation of pixels in a neighborhood can indicate the degree of variability of pixel values in that region. The following table lists these functions.

Function | Description |
---|---|

`rangefilt` | Calculates the local range of an image. |

`stdfilt` | Calculates the local standard deviation of an image. |

`entropyfilt` | Calculates the local entropy of a grayscale image. Entropy is a statistical measure of randomness. |

The functions all operate in a similar way: they define a neighborhood around the pixel of interest, calculate the statistic for that neighborhood, and use that value as the value of the pixel of interest in the output image.

This example shows how the `rangefilt`

function
operates on a simple array.

A = [ 1 2 3 4 5; 6 7 8 9 10; 11 12 13 14 15; 16 17 18 19 20 ] A = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 B = rangefilt(A) B = 6 7 7 7 6 11 12 12 12 11 11 12 12 12 11 6 7 7 7 6

The following figure shows how the value of element `B(2,4)`

was
calculated from `A(2,4)`

. By default, the `rangefilt`

function
uses a 3-by-3 neighborhood but you can specify neighborhoods of different
shapes and sizes.

**Determining Pixel Values in Range Filtered
Output Image**

The `stdfilt`

and `entropyfilt`

functions
operate similarly, defining a neighborhood around the pixel of interest
and calculating the statistic for the neighborhood to determine the
pixel value in the output image. The `stdfilt`

function
calculates the standard deviation of all the values in the neighborhood.

The `entropyfilt`

function calculates the entropy
of the neighborhood and assigns that value to the output pixel. Note
that, by default, the `entropyfilt`

function defines
a 9-by-9 neighborhood around the pixel of interest. To calculate the
entropy of an entire image, use the `entropy`

function.

This example shows how to detect regions of texture in an image using the texture filter functions.

Read in the image and display it.

```
I = imread('eight.tif');
imshow(I)
```

In the figure, the background is smooth; there is very little variation in the gray-level values. In the foreground, the surface contours of the coins exhibit more texture. In this image, foreground pixels have more variability and thus higher range values.

Filter the image with the `rangefilt`

function
and display the results. Range filtering makes the edges and contours
of the coins visible.

K = rangefilt(I); figure, imshow(K)

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