image =	localEntropy3dImg (inImg3d,inHalfKnlSizeX,inHalfKnlSizeY,inHalfKnlSizeZ)
image =	localEntropy3dImg (inImg3d,inHalfKnlSizeX,inHalfKnlSizeY,inHalfKnlSizeZ,inOptNbClasses)

Detailed Description

local 3d image entropy computation

This algorithm computes for each pixel of output image associated local entropy on a box neighbourhood of input image

Given an input image $InImg$ , box kernel half sizes $N_x = InHalfKnlSizeX$ , $N_y = InHakfKnlSizeY$ and $N_z = InHalfKnlSizeZ$ and a number of classes $n$ , output image values are given by :

$OutImg[x, y, z] = -\sum_{i=0}^{n}{p(i).log(p(i))}$

where:

corresponds to the natural logarithm function
equals to , with the number of pixels in kernel whose value belongs to the interval of the ith bin of the histogram computed on the box kernel, with following parameters:
- number of classes =
- $min_{histo}$ = minimum value of the whole input image
- $max_{histo}$ = maximum value of the whole input image

The number of classes is an optional parameter of the algorithm. Its default value equals to 16. If the number of classes specified by the user exceeds the maximum allowed number of classes given the input image data type and dynamic range, it is automatically adjusted. For instance:

a number of classes initialized to value 16 will be automatically adjusted to value 2 for a binary input image.
a number of classes initialized to value 256 will be automatically adjusted to value 10 for an integral input image whose dynamic range equals to

Borders of input image are processed as followed: padding pixels are a mirror reflection of the border pixels in the input image (see Border policy for more details).

Here is an example of an output image computed from the LocalEntropy3dImg algorithm on a 8-bits grey level, with a kernel of size 3 pixels x 4 pixels x 5 pixels and 8 classes:

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLStats as stats

Code Example

    # opening of input image
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    
    # local entropy 3d image computation
    outImg = stats.localEntropy3dImg(inImg, 3, 3, 3)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLStats/Processor/LocalEntropy3dImg/LocalEntropy3dImg.h>

Code Example

        // compute local entropy on input image
        ImagePtr pOutImg = localEntropy3dImg(pInImg, inHalfKnlSizeX, inHalfKnlSizeY, inHalfKnlSizeZ, nbClasses);