scalar = pearsonCorrelationCoefficient3d (inImg3d1,inImg3d2)

Detailed Description

computes the Pearson correlation coefficient in the 3d image

The Pearson correlation coefficient, also known as Pearson colocalization, is a linear correlation measure between the two input images InImg3d1 and InImg3d2. This coefficient is calculated with the following formula :

$PCC = \sum_{\textbf{x} \in \Omega}{\frac{ \left( InImg3d1[\textbf{x}] - \mu_1\right) \left( InImg3d2[\textbf{x}] - \mu_2\right)}{\sigma_1 \sigma_2}}$

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

Two wrappers can be called : the pearsonCorrelationCoefficient3d wrapper is only used to compute the Pearson coefficient on a grey level 3d image, whereas the multiSlice_pearsonCorrelationCoefficient3d wrapper must be used for more complex data (sequence and/or color). In the second case, a coefficient is calculated for each 3d volume forming the input images.

This algorithm is the 3d version of Pearson Correlation Coefficient 2d.

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

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLGlobalMeasure as glbmsr

Code Example

    # Sample a single slice result
    result = glbmsr.pearsonCorrelationCoefficient3d(inImg1, inImg2)
    
    # Sample a multislice result
    multislice_result = glbmsr.multiSlice_pearsonCorrelationCoefficient3d(inImg1, inImg2)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLGlobalMeasure/Processor/PearsonCorrelationCoefficient3d/PearsonCorrelationCoefficient3d.h>

Code Example

            // --------------------------------- Compute the Pearson colocalization a mono-slice grey level image ---------------------------------- //
            const ipReal64 pearsonCorrCoeff = pearsonCorrelationCoefficient3d(pInImg1, pInImg2);
            // ----------------------------------------------- Calculation on a multi-slice RGB image ---------------------------------------------- //
            PlanIndexedPearsonCCResultPtr pPearsonCorrCoeff_MultiSlice = multiSlice_pearsonCorrelationCoefficient3d(pImg1_multiSlice, pImg2_multiSlice);