[ITK] [ITK-users] 2D rigid transformation
Bill Lorensen
bill.lorensen at gmail.com
Tue May 19 11:20:06 EDT 2015
It would be useful if you included a compilable, minimal example.
I include your code snippet in a small program that reads two files
and performs the optimization. I ran it with the fixed and moving set
to the same image. Here are my results:
InitialPosition = [-6.283185307179586, 0, 0]
CurrentValue = 3528.15
NumberOfSteps = [6, 0, 0]
Stop = 1
StepLength = 1
CurrentIndex = [0, 0, 0]
MaximumMetricValue = 3528.15
MinimumMetricValue = 4.50585e-27
MinimumMetricValuePosition = [-6.283185307179586, 0, 0]
MaximumMetricValuePosition = [-3.141592653589793, 0, 0]
I suspect that your images are filled with zeroes. You do not show how
you populate the images.
Here is my code:
#include "itkImageFileReader.h"
#include "itkEuler2DTransform.h"
#include "itkExhaustiveOptimizerv4.h"
#include "itkMeanSquaresImageToImageMetricv4.h"
#include "itkCenteredTransformInitializer.h"
#include "itkImageRegistrationMethodv4.h"
#include "itkImage.h"
int main (int argc, char *argv[])
{
if (argc < 3)
{
std::cout << "Usage: " << argv[0] << " fixedImage movingImage" << std::endl;
return EXIT_FAILURE;
}
typedef itk::Image<double, 2> FixedImageType;
typedef itk::Image<double, 2> MovingImageType;
typedef itk::ImageFileReader<FixedImageType> FixedImageReaderType;
typedef itk::ImageFileReader<MovingImageType> MovingImageReaderType;
typedef itk::Euler2DTransform< double > TransformType;
typedef itk::ExhaustiveOptimizerv4< double > OptimizerType;
typedef itk::MeanSquaresImageToImageMetricv4< FixedImageType,
MovingImageType >
MetricType;
typedef itk::CenteredTransformInitializer< TransformType,
FixedImageType, MovingImageType >
TransformInitializerType;
typedef itk::ImageRegistrationMethodv4< FixedImageType,
MovingImageType, TransformType >
RegistrationType;
FixedImageReaderType::Pointer fixedImageReader =
FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader =
MovingImageReaderType::New();
FixedImageType::Pointer fixedImage = FixedImageType::New();
MovingImageType::Pointer movingImage = MovingImageType::New();
TransformType::Pointer transform = TransformType::New();
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
fixedImageReader->SetFileName (argv[1]);
fixedImageReader->Update();
fixedImage = fixedImageReader->GetOutput();
movingImageReader->SetFileName (argv[2]);
movingImageReader->Update();
movingImage = movingImageReader->GetOutput();
unsigned int angles = 12;
OptimizerType::StepsType steps( transform->GetNumberOfParameters() );
steps[0] = int(angles/2);
steps[1] = 0;
steps[2] = 0;
optimizer->SetNumberOfSteps( steps );
OptimizerType::ScalesType scales( transform->GetNumberOfParameters() );
scales[0] = 2.0 * vnl_math::pi / angles;
scales[1] = 1.0;
scales[2] = 1.0;
optimizer->SetScales( scales );
initializer->SetTransform( transform );
initializer->SetFixedImage( fixedImage );
initializer->SetMovingImage( movingImage );
initializer->InitializeTransform();
// Initialize registration
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImage );
registration->SetInitialTransform( transform );
try
{
registration->Update();
optimizer->Print(std::cout);
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
On Tue, May 19, 2015 at 5:41 AM, Pietro Nardelli
<p.nardelli at umail.ucc.ie> wrote:
> Hi guys,
>
> thank you very much for your help. I was trying to follow the examples you
> sent me (converting it in native C++ ITK code), but it seems that the
> optimizer does not work properly. In particular, no matter the number of
> angles and the step length I choose, it always gives me the same result. It
> seems to me that the optimizer is never updating the angles, therefore it is
> not following the grid I create. Also the metric is always 0. There is
> definitely something wrong! Here is part of the script I am using and the
> result I always get. Am I overlooking anything?
>
> typedef itk::Euler2DTransform< double >
> TransformType;
> typedef itk::ExhaustiveOptimizerv4< double >
> OptimizerType;
> typedef itk::MeanSquaresImageToImageMetricv4< FixedInputImageType,
> MovingInputImageType > MetricType;
> typedef itk::CenteredTransformInitializer< TransformType,
> FixedInputImageType, MovingInputImageType > TransformInitializerType;
> typedef itk::ImageRegistrationMethodv4< FixedInputImageType,
> MovingInputImageType, TransformType > RegistrationType;
>
> typename TransformType::Pointer transform =
> TransformType::New();
> typename MetricType::Pointer metric =
> MetricType::New();
> typename OptimizerType::Pointer optimizer =
> OptimizerType::New();
> typename RegistrationType::Pointer registration =
> RegistrationType::New();
> typename TransformInitializerType::Pointer initializer =
> TransformInitializerType::New();
>
> unsigned int angles = 12;
> OptimizerType::StepsType steps( transform->GetNumberOfParameters() );
> steps[0] = int(angles/2);
> steps[1] = 0;
> steps[2] = 0;
> optimizer->SetNumberOfSteps( steps );
>
> OptimizerType::ScalesType scales( transform->GetNumberOfParameters() );
> scales[0] = 2.0 * vnl_math::pi / angles;
> scales[1] = 1.0;
> scales[2] = 1.0;
>
> optimizer->SetScales( scales );
>
> initializer->SetTransform( transform );
> initializer->SetFixedImage( fixImage );
> initializer->SetMovingImage( movImage );
> //initializer->GeometryOn();
> initializer->InitializeTransform();
>
> // Initialize registration
> registration->SetMetric( metric );
> registration->SetOptimizer( optimizer );
> registration->SetFixedImage( fixedImage );
> registration->SetMovingImage( movingImage );
> registration->SetInitialTransform( transform );
>
> try
> {
> registration->Update();
> }
> catch( itk::ExceptionObject & err )
> {
> std::cerr << "ExceptionObject caught !" << std::endl;
> std::cerr << err << std::endl;
> return EXIT_FAILURE;
> }
>
> Result:
>
> Final parameters: [-1.8849555921538759, -40, 40]
> Result =
> Metric value = 0
> Angle (rad) = -1.88496
> Angle (degrees) = -108
> Iterations = 13
> Rotation Center = [225.5, 185.5]
> ExhaustiveOptimizerv4: Completed sampling of parametric space of size 3
>
>
> Thank you very much,
> Pietro
>
> Pietro Nardelli, MEngSc, BE
>
> Ph.D Candidate
>
> School of Engineering
>
> Electrical & Electronic Engineering
>
> University College Cork
>
> College Road
>
> Cork, Ireland
>
>
> On Fri, May 15, 2015 at 7:21 PM, Yaniv, Ziv Rafael (NIH/NLM/LHC) [C]
> <zivrafael.yaniv at nih.gov> wrote:
>>
>> Hello Pietro,
>>
>> You should use the ExhaustiveOptimizerv4
>> (http://www.itk.org/Doxygen/html/classitk_1_1ExhaustiveOptimizerv4.html)
>> which allows you to set a grid on which the similarity metric is evaluated.
>>
>> If you are familiar with python, then the following SimpleITK notebook may
>> be of use to you (see last section):
>> https://github.com/zivy/SimpleITK-Notebook-Staging/blob/master/registration3.ipynb
>>
>> regards
>> Ziv
>>
>> From: Pietro Nardelli
>> <p.nardelli at umail.ucc.ie<mailto:p.nardelli at umail.ucc.ie>>
>> Date: Friday, May 15, 2015 at 2:13 PM
>> To: "insight-users at itk.org<mailto:insight-users at itk.org>"
>> <insight-users at itk.org<mailto:insight-users at itk.org>>
>> Subject: [ITK-users] 2D rigid transformation
>>
>> Hello guys,
>>
>> is there a way to have a 2D rigid registration that uses a specific number
>> of rotations and chooses the best one? I have two images that are simply
>> rotated with respect to each other, and I would like to register them using
>> for example 36 rotations (therefore computing the mean squared error every
>> 10 degrees). At the moment I am using the 2DRigidTransform with a specified
>> center, with a regular step descent optimizer and the
>> ImageRegistrationMethodv4. I saw that the transform has the function
>> SetFixedParameters() but I am not really sure whether I understand correctly
>> that that would tell the optimizer the angles (and translations) to use at
>> every iteration. Could anyone please clarify this?
>>
>> Thank you very much,
>> Pietro
>>
>>
>
>
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