# Are there any instances about the registration of 3D medical image by landmark?
**URL:** https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256
**Category:** Beginner Questions
**Tags:** itk, registration
**Created:** [October 18, 2023, 6:34am UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256 "2023-10-18T06:34:14Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@SeanHan](https://discourse.itk.org/u/SeanHan)
#### Post date: [October 18, 2023, 6:34am UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/1 "2023-10-18T06:34:14Z")
When I set LandmarkBasedTransformInitializer instead of CenteredTransformInitializer, it seems a wrong result. Any suggestions?
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### Author:  [@matt.mccormick](https://discourse.itk.org/u/matt.mccormick)
#### Post date: [October 18, 2023, 2:12pm UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/2 "2023-10-18T14:12:57Z")
Hi,
Here is a [LandmarkBasedTransformInitializer example](https://examples.itk.org/src/registration/common/registerimagetoanotherusinglandmarks/documentation).
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### Author:  [@SeanHan](https://discourse.itk.org/u/SeanHan)
#### Post date: [October 19, 2023, 1:20am UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/3 "2023-10-19T01:20:49Z")
Many thanks, but I think 3D landmark is quite difference from 2D landmark.
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### Author:  [@matt.mccormick](https://discourse.itk.org/u/matt.mccormick)
#### Post date: [October 19, 2023, 2:45am UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/4 "2023-10-19T02:45:20Z")
ITK handles both 2D and 3D.
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### Author:  [@SeanHan](https://discourse.itk.org/u/SeanHan)
#### Post date: [October 19, 2023, 5:51am UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/5 "2023-10-19T05:51:36Z")
The following is my code, is that correct? I can’t get the right output from this registration.
```
using TransformType = itk::VersorRigid3DTransform;
using OptimizerType = itk::RegularStepGradientDescentOptimizerv4;
using MetricType = itk::MattesMutualInformationImageToImageMetricv4;
using RegistrationType = itk::
ImageRegistrationMethodv4;
auto metric = MetricType::New();
auto optimizer = OptimizerType::New();
auto registration = RegistrationType::New();
registration->SetMetric(metric);
unsigned int iBins = 24;
metric->SetNumberOfHistogramBins(iBins);
metric->SetUseFixedImageGradientFilter(false);
metric->SetUseMovingImageGradientFilter(false);
using LandmarkBasedTransformInitializerType =
itk::LandmarkBasedTransformInitializer;
LandmarkBasedTransformInitializerType::Pointer landmarkBasedTransformInitializer =
LandmarkBasedTransformInitializerType::New();
// Create source and target landmarks.
using LandmarkContainerType = LandmarkBasedTransformInitializerType::LandmarkPointContainer;
using LandmarkPointType = LandmarkBasedTransformInitializerType::LandmarkPointType;
LandmarkContainerType fixedLandmarks;
LandmarkContainerType movingLandmarks;
LandmarkPointType fixedPoint;
LandmarkPointType movingPoint;
for (size_t i = 0; i < fixedpoints.size(); i++)
{
fixedPoint[0] = fixedpoints[i].x;
fixedPoint[1] = fixedpoints[i].y;
fixedPoint[2] = fixedpoints[i].z;
fixedLandmarks.push_back(fixedPoint);
}
for (size_t i = 0; i < movedpoints.size(); i++)
{
movingPoint[0] = movedpoints[i].x;
movingPoint[1] = movedpoints[i].y;
movingPoint[2] = movedpoints[i].z;
movingLandmarks.push_back(movingPoint);
}
landmarkBasedTransformInitializer->SetFixedLandmarks(fixedLandmarks);
landmarkBasedTransformInitializer->SetMovingLandmarks(movingLandmarks);
auto transform = TransformType::New();
transform->SetIdentity();
using VersorType = TransformType::VersorType;
using VectorType = VersorType::VectorType;
VersorType rotation;
VectorType axis;
axis[0] = 0.0;
axis[1] = 0.0;
axis[2] = 1.0;
constexpr double angle = 0;
rotation.Set(axis, angle);
transform->SetRotation(rotation);
landmarkBasedTransformInitializer->SetTransform(transform);
landmarkBasedTransformInitializer->SetReferenceImage(casterfix->GetOutput());
landmarkBasedTransformInitializer->InitializeTransform();
registration->SetOptimizer(optimizer);
registration->SetFixedImage(casterfix->GetOutput());
registration->SetMovingImage(castermove->GetOutput());
registration->SetInitialTransform(transform);
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
const double translationScale = 1.0 / 1000.0;
optimizerScales[0] = 1.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = 1.0;
optimizerScales[3] = translationScale;
optimizerScales[4] = translationScale;
optimizerScales[5] = translationScale;
optimizer->SetScales(optimizerScales);
optimizer->SetNumberOfIterations(100);
optimizer->SetLearningRate(0.5);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetReturnBestParametersAndValue(true);
optimizer->SetRelaxationFactor(0.8);
auto observer = CommandIterationUpdatev4::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
constexpr unsigned int numberOfLevels = 3;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize(3);
shrinkFactorsPerLevel[0] = 4;
shrinkFactorsPerLevel[1] = 2;
shrinkFactorsPerLevel[2] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(3);
smoothingSigmasPerLevel[0] = 2;
smoothingSigmasPerLevel[0] = 1;
smoothingSigmasPerLevel[0] = 1;
registration->SetNumberOfLevels(numberOfLevels);
registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
RegistrationType::MetricSamplingStrategyEnum samplingStrategy =
RegistrationType::MetricSamplingStrategyEnum::RANDOM;
double samplingPercentage = 0.2;
registration->SetMetricSamplingStrategy(samplingStrategy);
registration->SetMetricSamplingPercentage(samplingPercentage);
registration->MetricSamplingReinitializeSeed(121213);
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
}
```
---
### Author:  [@matt.mccormick](https://discourse.itk.org/u/matt.mccormick)
#### Post date: [October 20, 2023, 8:19pm UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/6 "2023-10-20T20:19:45Z")
There are extra calls that are not needed, but I do not see anything standing out that would cause an issue. Was the landmark-based initialization evaluated outside the broader registration?
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### Author:  [@SeanHan](https://discourse.itk.org/u/SeanHan)
#### Post date: [October 23, 2023, 7:16am UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/7 "2023-10-23T07:16:06Z")
I just don’t know whether this is the right process of registration based on landmark or not.
---
### Author:  [@dzenanz](https://discourse.itk.org/u/dzenanz)
#### Post date: [October 23, 2023, 2:32pm UTC](https://discourse.itk.org/t/are-there-any-instances-about-the-registration-of-3d-medical-image-by-landmark/6256/8 "2023-10-23T14:32:11Z")
Registering based on landmarks should be as simple as this:
>
The code in C++ should be only a little bit more verbose, but otherwise the same.