image =	darkHysteresisThreshold3dImg (inImg3d,highSeedThreshold,highPropagationThreshold)
image =	lightHysteresisThreshold3dImg (inImg3d,lowSeedThreshold,lowPropagationThreshold)
image =	hysteresisThreshold3dImg (inImg3d,lowSeedThreshold,highSeedThreshold,lowPropagationThreshold,highPropagationThreshold)

Detailed Description

Apply hysteresis thresholding to a 3d image.

Hysteresis threshold algorithm applies two thresholds $T_S$ and $T_P$ on the output image. $T_S$ is the most restrictive threshold and yields a seed image $I_S$ , used to propagate the marked features in $I_P$ , obtained by thresholding the input image by $T_P$ . This algorithm is commonly used in edge detection such as Canny edge detector.

The aim of this threshold is to preserve the features in $I_P$ containing a seed in $I_S$ .

The algorithm needs 4 thresholds: the minimum and maximum thresholds $T_S^{Min}$ and $T_S^{Max}$ to compute the seed image $I_S$ and the minimum and maximum thresholds $T_P^{Min}$ and $T_P^{Max}$ to compute the image $I_P$ , used for the propagation.

$\begin{eqnarray*} I_S[i] & = & \begin{cases} 1, & \text{if } T_S^{Min} \leq InImg[i] \leq T_S^{Max} \\ 0, & \text{otherwise} \end{cases} \\ I_P[i] & = & \begin{cases} 1, & \text{if } T_P^{Min} \leq InImg[i] \leq T_P^{Max} \\ 0, & \text{otherwise} \end{cases} \end{eqnarray*}$

To simplify the algorithm parametrization, several wrappers are defined to apply this threshold:

darkHysteresisThreshold3dImg: this wrapper only needs $T_S^{Max}$ and $T_P^{Max}$ , $T_S^{Min}$ and $T_S^{Min}$ being automatically set to the image buffer type minimum value. This allows to select dark features using darker seeds on light background.
lightHysteresisThreshold3dImg: this wrapper only needs $T_S^{Min}$ and $T_P^{Min}$ , $T_S^{Max}$ and $T_S^{Max}$ being automatically set to the image buffer type maximum value. This allows to select light features using lighter seeds on dark background.
hysteresisThreshold3dImg: this wrapper allows the user to determine the four thresholds. However, the following rules must be respected:
$\left\{ \begin{array}{ccc} T_S^{Min} & \leq & T_S^{Max} \\ T_P^{Min} & \leq & T_P^{Max} \\ T_S^{Max} & \leq & T_P^{Max} \end{array} \right.$

See Hysteresis Threshold 2d for an example of hysteresis threshold.

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLBinarization as bin

Code Example

    # opening of input images
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    
    # threshold computation
    outImg = bin.hysteresisThreshold3dImg(inImg,  100,  127, 64, 127)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLBinarization/Processor/HysteresisThreshold3dImg/HysteresisThreshold3dImg.h>

Code Example

            // Compute the dark hysteresis threshold
            // --------------------------------------
            ImagePtr pOutImg_dark = darkHysteresisThreshold3dImg(pInImg, threshSeedMax_dark, threshPropagationMax_dark);
            // Compute the light hysteresis threshold
            // --------------------------------------
            ImagePtr pOutImg_light = lightHysteresisThreshold3dImg(pInImg, threshSeedMin_light, threshPropagationMin_light);
            // Compute the hysteresis threshold with
            // custom params and provided ouput image
            // --------------------------------------
            const ipUInt64 sizeX = pInImg->getSizeX();
            const ipUInt64 sizeY = pInImg->getSizeY();
            const ipUInt64 sizeZ = pInImg->getSizeZ();
            // Create output image
            ImageGeometryPtr pOutputImageGeometry = geometry3d(eImageBufferType::eIBT_Binary, sizeX, sizeY, sizeZ);
            boost::shared_ptr<MemoryImage> pOutImg(boost::make_shared<MemoryImage>());
            pOutImg->init(*pOutputImageGeometry);
            // Compute the threshold
            hysteresisThreshold3dImg(pInImg, threshSeedMin, threshSeedMax, threshPropagationMin, threshPropagationMax, neighbourhood3d, pOutImg);