Normalize

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This node performs linear normalization of input data to fit within a specified intensity range. The normalization is calculated using the following equation:

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

Parameters and Definitions

\(I\): Input to be normalized.
\(I_{norm}\): Normalized output.
\(\text{min}(I)\): Minimum value in the input data.
\(\text{max}(I)\): Maximum value in the input data.
\(\text{min}_{norm}\) and \(\text{max}_{norm}\): Desired range for the normalized output. For example, setting \(\text{min}_{norm}\) to 0 and \(\text{max}_{norm}\) to 1 scales all input values between 0 and 1.

\(\text{min}(I)\) and \(\text{max}(I)\) can be calculated from the entire ND-image, a 3D volume, or on a slice-by-slice basis. These values can also be constrained within an optional input mask.

If complex data is provided, the real and imaginary components are normalized separately.

Inputs

Input

Input image to be normalized.

Type: Image, List, Required, Single

Mask

Optional mask that defines the region from which the minimum and maximum values are extracted.

Type: Mask, Optional, Single

Outputs

Output

Normalized output image.

Type: Image, List

Settings

Normalize Selection

Specify how normalization should be performed:

Per Slice Normalizes each slice independently.
Per Volume Normalizes each 3D volume independently.
Entire Image Normalizes the entire ND-image as a whole.

Values: Per Slice, Per Volume, Entire Image

Min Float

Specifies the lowest possible value in the output. All normalized values are scaled so that the smallest input value matches this minimum.

Max Float

Specifies the highest possible value in the output. The largest input value is scaled to match this value during normalization.