ITK  5.2.0 Insight Toolkit
Examples/Filtering/ResampleVolumesToBeIsotropic.cxx
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// Software Guide : BeginLatex
//
// It is unfortunate that it is still very common to find medical image
// datasets that have been acquired with large inter-slice spacings that
// result in voxels with anisotropic shapes. In many cases these voxels have
// ratios of $[1:5]$ or even $[1:10]$ between the resolution in the plane
// $(x,y)$ and the resolution along the $z$ axis. These datasets are close to
// \textbf{useless} for the purpose of computer-assisted image analysis. The
// abundance of datasets acquired with anisotropic voxel sizes bespeaks a
// dearth of understanding of the third dimension and its importance for
// Datasets acquired with large anisotropies bring with them the regressive
// message: \emph{I do not think 3D is informative''}.
// They stubbornly insist: \emph{all that you need to know, can be known
// by looking at individual slices, one by one''}. However, the fallacy of
// this statement is made evident by simply viewing the slices when
// reconstructed in any of the orthogonal planes. The rectangular pixel shape
// is ugly and distorted, and cripples any signal processing algorithm not
// designed specifically for this type of image.
//
// Image analysts have a long educational battle to fight in the radiological
// setting in order to bring the message that 3D datasets acquired with
// anisotropies larger than $[1:2]$ are simply dismissive of the most
// fundamental concept of digital signal processing: The Shannon Sampling
// Theorem~\cite{Shannon1948,Shannon1949}.
//
// Facing the inertia of many clinical imaging departments and their
// blithe insistence that these images are good enough''
// for image processing, some image analysts have stoically tried
// to deal with these poor datasets. These image analysts usually
// proceed to subsample the high in-plane resolution and to super-sample the
// inter-slice resolution with the purpose of faking the type of dataset that
// they should have received in the first place: an \textbf{isotropic}
// dataset. This example is an illustration of how such an operation can be
// performed using the filters available in the Insight Toolkit.
//
// Note that this example is not presented here as a \emph{solution} to the
// problem of anisotropic datasets. On the contrary, this is simply a
// \emph{dangerous palliative} which will only perpetuate the errant
// convictions of image acquisition departments. The real solution to the
// problem of the
// anisotropic dataset is to educate radiologists regarding the
// principles of image processing. If you really care about the technical
// decency of the medical image processing field, and you really care about
// providing your best effort to the patients who will receive health care
// directly or indirectly affected by your processed images, then it is your
// duty to reject anisotropic datasets and to patiently explain to your
// radiologist why anisotropic data are problematic for processing, and
// require crude workarounds which handicap your ability to draw accurate
// conclusions from the data and preclude his or her ability to provide
// quality care. Any barbarity such as a $[1:5]$ anisotropy ratio should be
// considered as a mere collection of slices, and not an authentic 3D dataset.
//
// Please, before employing the techniques covered in this section, do kindly
// invite your fellow radiologist to see the dataset in an orthogonal
// slice. Magnify that image in a viewer without any linear interpolation
// until you see the daunting reality of the rectangular pixels. Let her/him
// know how absurd it is to process digital data which have been sampled at
// ratios of $[1:5]$ or $[1:10]$. Then, inform them that your only option is
// to throw away all that high in-plane
// resolution and to \emph{make up} data between the slices in order to
// compensate for the low resolution. Only then will you be justified in
// using the following code.
//
// \index{Anisotropic data sets}
// \index{Subsampling}
// \index{Supersampling}
// \index{Resampling}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Let's now move into the code. It is appropriate for you to experience
// guilt\footnote{A feeling of regret or remorse for having committed some
// improper act; a recognition of one's own responsibility for doing something
// wrong.}, because your use the code below is the
// evidence that we have lost one more battle on the quest for real 3D dataset
// processing.
//
// This example performs subsampling on the in-plane resolution and performs
// super-sampling along the inter-slices resolution. The subsampling process
// requires that we preprocess the data with a smoothing filter in order to
// avoid the occurrence of aliasing effects due to overlap of the spectrum in
// the frequency domain~\cite{Shannon1948,Shannon1949}. The smoothing is
// performed here using the \code{RecursiveGaussian} filter, because it
// provides a convenient run-time performance.
//
// The first thing that you will need to do in order to resample this ugly
// anisotropic dataset is to include the header files for the
// \doxygen{ResampleImageFilter}, and the Gaussian smoothing filter.
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The resampling filter will need a Transform in order to map point
// coordinates and will need an interpolator in order to compute intensity
// values for the new resampled image. In this particular case we use the
// \doxygen{IdentityTransform} because the image is going to be resampled by
// preserving the physical extent of the sampled region. The Linear
// interpolator is used as a common trade-off\footnote{Although arguably we
// should use one type of interpolator for the in-plane subsampling process
// and another one for the inter-slice supersampling. But again, one should
// wonder why we apply any technical sophistication here, when we are covering
// up for an improper acquisition of medical data, trying to make it look as
// if it was correctly acquired.}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that, as part of the preprocessing of the image, in this example we
// are also rescaling the range of intensities. This operation has already
// been described as Intensity Windowing. In a real clinical application, this
// step requires careful consideration of the range of intensities that
// contain information about the anatomical structures that are of interest
// for the current clinical application. It practice you may want to remove
// this step of intensity rescaling.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 5)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile lower upper "
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We make explicit now our choices for the pixel type and dimension of the
// input image to be processed, as well as the pixel type that we intend to
// use for the internal computation during the smoothing and resampling.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 3;
using InputPixelType = unsigned short;
using InternalPixelType = float;
using InputImageType = itk::Image<InputPixelType, Dimension>;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
// Software Guide : EndCodeSnippet
try
{
}
catch (const itk::ExceptionObject & excep)
{
std::cerr << "Exception caught!" << std::endl;
std::cerr << excep << std::endl;
}
using IntensityFilterType =
IntensityFilterType::Pointer intensityWindowing =
IntensityFilterType::New();
intensityWindowing->SetWindowMinimum(std::stoi(argv[3]));
intensityWindowing->SetWindowMaximum(std::stoi(argv[4]));
intensityWindowing->SetOutputMinimum(0.0);
intensityWindowing->SetOutputMaximum(
255.0); // floats but in the range of chars.
// Software Guide : BeginLatex
//
// We instantiate the smoothing filter that will be used on the
// preprocessing for subsampling the in-plane resolution of the dataset.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using GaussianFilterType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We create two instances of the smoothing filter: one will smooth along
// the $X$ direction while the other will smooth along the $Y$ direction.
// They are connected in a cascade in the pipeline, while taking their input
// from the intensity windowing filter. Note that you may want to skip the
// intensity windowing scale and simply take the input directly from the
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
GaussianFilterType::Pointer smootherX = GaussianFilterType::New();
GaussianFilterType::Pointer smootherY = GaussianFilterType::New();
smootherX->SetInput(intensityWindowing->GetOutput());
smootherY->SetInput(smootherX->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We must now provide the settings for the resampling itself. This is done
// by searching for a value of isotropic resolution that will provide a
// trade-off between the evil of subsampling and the evil of supersampling.
// We advance here the conjecture that the geometrical mean between the
// in-plane and the inter-slice resolutions should be a convenient isotropic
// resolution to use. This conjecture is supported on nothing other than
// intuition and common sense. You can rightfully argue that this choice
// deserves a more technical consideration, but then, if you are so
// concerned about the technical integrity of the image sampling process,
// you should not be using this code, and should discuss these issues with
// the radiologist who acquired this ugly anisotropic dataset.
//
// We take the image from the input and then request its array of pixel
// spacing values.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const InputImageType::SpacingType & inputSpacing = inputImage->GetSpacing();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// and apply our ad-hoc conjecture that the correct anisotropic resolution
// to use is the geometrical mean of the in-plane and inter-slice
// resolutions. Then set this spacing as the Sigma value to be used for the
// Gaussian smoothing at the preprocessing stage.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const double isoSpacing = std::sqrt(inputSpacing[2] * inputSpacing[0]);
smootherX->SetSigma(isoSpacing);
smootherY->SetSigma(isoSpacing);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We instruct the smoothing filters to act along the $X$ and $Y$ direction
// respectively.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
smootherX->SetDirection(0);
smootherY->SetDirection(1);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now that we have taken care of the smoothing in-plane, we proceed to
// instantiate the resampling filter that will reconstruct an isotropic
// image. We start by declaring the pixel type to be used as the output of
// this filter, then instantiate the image type and the type for the
// resampling filter. Finally we construct an instantiation of the filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using ResampleFilterType =
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The resampling filter requires that we provide a Transform, which in this
// particular case can simply be an identity transform.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
transform->SetIdentity();
resampler->SetTransform(transform);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter also requires an interpolator to be passed to it. In this case
// we chose to use a linear interpolator.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InterpolatorType =
InterpolatorType::Pointer interpolator = InterpolatorType::New();
resampler->SetInterpolator(interpolator);
// Software Guide : EndCodeSnippet
resampler->SetDefaultPixelValue(255); // highlight regions without source
// Software Guide : BeginLatex
//
// The pixel spacing of the resampled dataset is loaded in a
// \code{SpacingType} and passed to the resampling filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OutputImageType::SpacingType spacing;
spacing[0] = isoSpacing;
spacing[1] = isoSpacing;
spacing[2] = isoSpacing;
resampler->SetOutputSpacing(spacing);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The origin and orientation of the output image is maintained, since we
// decided to resample the image in the same physical extent of the input
// anisotropic image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
resampler->SetOutputOrigin(inputImage->GetOrigin());
resampler->SetOutputDirection(inputImage->GetDirection());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The number of pixels to use along each dimension in the grid of the
// resampled image is computed using the ratio between the pixel spacings of
// the input image and those of the output image. Note that the computation
// of the number of pixels along the $Z$ direction is slightly different
// with the purpose of making sure that we don't attempt to compute pixels
// that are outside of the original anisotropic dataset.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
inputImage->GetLargestPossibleRegion().GetSize();
const double dx = inputSize[0] * inputSpacing[0] / isoSpacing;
const double dy = inputSize[1] * inputSpacing[1] / isoSpacing;
const double dz = (inputSize[2] - 1) * inputSpacing[2] / isoSpacing;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally the values are stored in a \code{SizeType} and passed to the
// resampling filter. Note that this process requires a casting since the
// computations are performed in \code{double}, while the elements of the
// \code{SizeType} are integers.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
size[0] = static_cast<SizeValueType>(dx);
size[1] = static_cast<SizeValueType>(dy);
size[2] = static_cast<SizeValueType>(dz);
resampler->SetSize(size);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Our last action is to take the input for the resampling image filter from
// the output of the cascade of smoothing filters, and then to trigger the
// execution of the pipeline by invoking the \code{Update()} method on the
// resampling filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
resampler->SetInput(smootherY->GetOutput());
resampler->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// At this point we should take a moment in silence to reflect on the
// circumstances that have led us to accept this cover-up for the improper
// acquisition of medical data.
//
// Software Guide : EndLatex
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
writer->SetInput(resampler->GetOutput());
try
{
writer->Update();
}
catch (const itk::ExceptionObject & excep)
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}
return EXIT_SUCCESS;
}
itkRecursiveGaussianImageFilter.h
itk::IdentityTransform
Implementation of an Identity Transform.
Definition: itkIdentityTransform.h:50
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itkImage.h
itkIntensityWindowingImageFilter.h
Data source that reads image data from a single file.
itk::LinearInterpolateImageFunction
Linearly interpolate an image at specified positions.
Definition: itkLinearInterpolateImageFunction.h:50
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::IntensityWindowingImageFilter
Applies a linear transformation to the intensity levels of the input Image that are inside a user-def...
Definition: itkIntensityWindowingImageFilter.h:149
itkIdentityTransform.h
itkImageFileWriter.h
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:90
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::RecursiveGaussianImageFilter
Base class for computing IIR convolution with an approximation of a Gaussian kernel.
Definition: itkRecursiveGaussianImageFilter.h:100
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itk::SizeValueType
unsigned long SizeValueType
Definition: itkIntTypes.h:83
itk::Size::GetSize
const SizeValueType * GetSize() const
Definition: itkSize.h:169