# AHE

Class: NodeAdaptiveHistogramEqualization

Histogram equalization modifies the contrast in an image. By modifying parameters (alpha, beta, and radius), the filter can produce an adaptively equalized histogram or a version of unsharp mask (local mean subtraction). Instead of applying a strict histogram equalization in a window about a pixel, this filter prescribes a mapping function (power law) controlled by the parameters alpha and beta.

## Inputs

#### Image

Input image.

Type: Image4DFloat, Required, Single

## Outputs

#### Output

Resulting image.

Type: Image4DFloat

## Settings

### Adaptive Histogram Equalization

#### Alpha Number

Controls how much the filter acts like the classical histogram equalization method (alpha=0) to how much the filter acts like an unsharp mask (alpha=1).

#### Beta Number

Controls how much the filter acts like an unsharp mask (beta=0) to much the filter acts like pass through (beta=1, with alpha=1).

### Region

#### Radius X Integer

Size of the region over which local statistics are calculated in the X direction, specified in voxels.

#### Radius Y Integer

Size of the region over which local statistics are calculated in the Y direction, specified in voxels.

#### Radius Z Integer

Size of the region over which local statistics are calculated in the Z direction, specified in voxels.

## References

1. “The Insight Segmentation and Registration Toolkit” www.itk.org