image = pearsonColocalization2dImg (inImg1,inImg2)

Detailed Description

builds the Pearson's colocalization map computing the Pearson correlation coefficient on each pixel

The Pearson colocalization, also known as Pearson correlation coefficient, is a linear correlation measure between the two input images InImg1 and InImg2. The result is a Real32 image where the intensity $OutRealImg[\textbf{x}]$ at the pixel at position $\textbf{x} = \left[ x, y \right]$ is calculated with the following formula :

$OutRealImg[\textbf{x}] = \frac{ \left( InImg1[\textbf{x}] - \mu_1\right) \left( InImg2[\textbf{x}] - \mu_2\right)}{\sigma_1 \sigma_2}$

Where $\mu_i, i \in [1, 2]$ is the mean intensity of $InImg_i$ and $\sigma_i$ is its standard deviation.

Here is an example of Pearson colocalization mapping:

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

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLFiltering as filter

Code Example

    # Sample with a generated output image
    outAutoImg = filter.pearsonColocalization2dImg(inImg1, inImg2)
    
    # Sample with a provided output image
    outImg = PyIPSDK.createImage(PyIPSDK.eImageBufferType.eIBT_Real32, inImg1.getSizeX(), inImg1.getSizeY())
    filter.pearsonColocalization2dImg(inImg1, inImg2, outImg)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLFiltering/Processor/PearsonColocalization2dImg/PearsonColocalization2dImg.h>

Code Example

            // Sample with a generated output image
            // ------------------------------------
            // compute the pearson colocalization
            ImagePtr pAutoOutImg = pearsonColocalization2dImg(pInImg1, pInImg2);
            // Sample with a provided output image
            // -----------------------------------
            // create output image
            ImageGeometryPtr pOutputImageGeometry = geometry2d(eImageBufferType::eIBT_Real32, sizeX, sizeY);
            boost::shared_ptr<MemoryImage> pOutImg(boost::make_shared<MemoryImage>());
            pOutImg->init(*pOutputImageGeometry);
            // compute addition of input images
            pearsonColocalization2dImg(pInImg1, pInImg2, pOutImg);