image =	standardizeImg (inImg)
image =	standardizeMaskImg (inImg,inOptMaskImg)

Detailed Description

Computes the z-score for each color plan and each frame.

This algorithm computes a standard score, also known as Z-score, for each pixel. The input image intensities are transformed in order to center them around 0 and rescale them according to the input image standard deviation. For each plan, along the color and temporal dimensions, the new intensities are calculated thanks to the following formula :

$OutRealImg[\textbf{x}] = \frac{InImg[\textbf{x}] - \mu}{\sigma}$

Where $\textbf{x}$ is the pixel 2d or 3d coodinate (i.e. along the x, y and eventually z axis), $\mu$ is the mean intensity for a given color and temporal plan and $\sigma$ is the corresponding standard deviation.

Note: The output image has float data type.

Here is an example of a standardization on a UInt8 gray-level with their intensity ranges:

See also: https://en.wikipedia.org/wiki/Standard_score

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLIntensityTransform as itrans

Code Example

    # opening of input images
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    
    # Z-score calculation
    outImg = itrans.standardizeImg(inImg)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLIntensityTransform/Processor/StandardizeImg/StandardizeImg.h>

Code Example

    // Standardization without mask
    // ============================
    ImagePtr pOutImg = standardizeImg(pInImg);
    // Standardization with mask
    // ============================
    ImagePtr pMaskedOutImg = standardizeMaskImg(pInImg, pMaskImg);