Normalize

Node Icon

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

\[ \begin{equation} V_{norm} = (V-\text{min}(V))\frac{\text{max}_{norm}-\text{min}_{norm}}{\text{max}(V)-\text{min}(V)}+\text{min}_{norm} \label{eq:normalize} \end{equation} \]

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.