# Normalize

Performs linear normalization of input data to fit within a specified intensity range. The function is defined by the following equation:

$$$V_{norm} = (V-\text{min}(V))\frac{\text{max}_{norm}-\text{min}_{norm}}{\text{max}(V)-\text{min}(V)}+\text{min}_{norm} \label{eq:normalize}$$$

#### Parameters and Definitions

• $$V$$: The input values to be normalized.
• $$V_{norm}$$: The result after normalization.
• $$\text{min}(V)$$: The minimum value found in the input data.
• $$\text{max}(V)$$: The maximum value found in the input data.
• $$\text{min}_{norm}$$ and $$\text{max}_{norm}$$: These define the desired range for the normalized output. For example, setting $$\text{min}_{norm}$$ = 0 and $$\text{max}_{norm}$$ = 1 would scale all input values to fall within the range of 0 to 1.

For image inputs, the $$\text{min}(V)$$ and $$\text{max}(V)$$ values can be taken from the entire ND-image, the 3D-volume or on a slice-by-slice basis.

If complex data is supplied, the real and imaginary parts are normalized independently.

## Inputs

#### Input

Input.

Type: Image, Numeric Array, Column, List, Required, Single

## Outputs

#### Output

Output.

Type: Image, List

## Settings

#### Normalize Selection

Set how to perform the normalization:

Per Slice normalizes each slice separately,
Per Volume normalizes each 3D-volume separatly and
Entire Image normalizes the entire ND-image.

Values: Per Slice, Per Volume, Entire Image

#### Min Float

Defines the lowest possible value in the output. All normalized values are scaled such that the smallest value aligns with this minimum value.

#### Max Float

Defines the highest possible value in the output. During normalization, the largest value is scaled to match this value.