ITK  6.0.0
Insight Toolkit
Examples/IO/VisibleHumanStreamReadWrite.cxx
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* Copyright NumFOCUS
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#include "itkRawImageIO.h"
// The Insight Toolkit was originally motivated by a need for software
// tools to segment and register the National Library of Medicine’s
// Visible Human Project data sets. The data is freely available
// through NLM’s website [3]. The original Visible Male cryosectional
// images are non-interlaced 24-bit RGB pixels with a resolution of
// 2048x1216 pixels by 1871 slices with a physical spacing of
// approximately 0.33 mm in slice and 1.0 mm between slices. These
// dimensions results in about 13 gigabytes of data, which is an
// appropriate size to demonstrate streaming. The following are two
// examples of streaming which shows all three IO classes capable of
// streaming along with the two types of streaming supported by the
// writer.
//
// A coronal slice is a classic view of the Visible Male. The
// following is an example that reads the entire raw dataset and
// generates that classic image:
int
main(int argc, char * argv[])
{
if (argc < 3)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " visibleHumanPath outputImageFile" << std::endl;
return EXIT_FAILURE;
}
std::string visibleHumanPath = argv[1];
std::string outputImageFile = argv[2];
using RGBPixelType = itk::RGBPixel<unsigned char>;
using PixelType = unsigned char;
using ImageType = itk::Image<PixelType, 3>;
using RGB3DImageType = itk::Image<RGBPixelType, 3>;
using RGB2DImageType = itk::Image<RGBPixelType, 2>;
// generate the names of the decompressed Visible Male images
using NameGeneratorType = itk::NumericSeriesFileNames;
auto nameGenerator = NameGeneratorType::New();
nameGenerator->SetSeriesFormat(visibleHumanPath + "a_vm%04d.raw");
nameGenerator->SetStartIndex(1001);
nameGenerator->SetEndIndex(2878);
nameGenerator->SetIncrementIndex(1);
// create an ImageIO for the red channel
using ImageIOType = itk::RawImageIO<PixelType, 2>;
auto rimageio = ImageIOType::New();
rimageio->SetDimensions(0, 2048);
rimageio->SetDimensions(1, 1216);
rimageio->SetSpacing(0, .33);
rimageio->SetSpacing(1, .33);
rimageio->SetHeaderSize(rimageio->GetImageSizeInPixels() * 0);
// create an ImageIO for the green channel
auto gimageio = ImageIOType::New();
gimageio->SetDimensions(0, 2048);
gimageio->SetDimensions(1, 1216);
gimageio->SetSpacing(0, .33);
gimageio->SetSpacing(1, .33);
gimageio->SetHeaderSize(gimageio->GetImageSizeInPixels() * 1);
// create an ImageIO for the blue channel
auto bimageio = ImageIOType::New();
bimageio->SetDimensions(0, 2048);
bimageio->SetDimensions(1, 1216);
bimageio->SetSpacing(0, .33);
bimageio->SetSpacing(1, .33);
bimageio->SetHeaderSize(bimageio->GetImageSizeInPixels() * 2);
using SeriesReaderType = itk::ImageSeriesReader<ImageType>;
auto rreader = SeriesReaderType::New();
rreader->SetFileNames(nameGenerator->GetFileNames());
rreader->SetImageIO(rimageio);
// the z-spacing will default to be correctly 1mm
auto greader = SeriesReaderType::New();
greader->SetFileNames(nameGenerator->GetFileNames());
greader->SetImageIO(gimageio);
auto breader = SeriesReaderType::New();
breader->SetFileNames(nameGenerator->GetFileNames());
breader->SetImageIO(bimageio);
using ComposeRGBFilterType =
auto composeRGB = ComposeRGBFilterType::New();
composeRGB->SetInput1(rreader->GetOutput());
composeRGB->SetInput2(greader->GetOutput());
composeRGB->SetInput3(breader->GetOutput());
// this filter is needed if square pixels are needed
// const int xyShrinkFactor = 3;
// using ShrinkImageFilterType = itk::ShrinkImageFilter< RGB3DImageType,
// RGB3DImageType >; auto shrinker =
// ShrinkImageFilterType::New(); shrinker->SetInput(
// composeRGB->GetOutput() ); shrinker->SetShrinkFactors( xyShrinkFactor
// ); shrinker->SetShrinkFactor( 2, 1 );
// update output information to know propagate the size of the largest
// possible region
composeRGB->UpdateOutputInformation();
composeRGB->GetOutput()->GetLargestPossibleRegion();
coronalSlice.SetIndex(1, 448);
coronalSlice.SetSize(1, 0);
// another interesting view
// RGB3DImageType::RegionType sagittalSlice =
// shrinker->GetOutput()->GetLargestPossibleRegion();
// sagittalSlice.SetIndex( 0, 1024 ); sagittalSlice.SetSize( 0, 0 );
// create a 2D coronal slice from the volume
using ExtractFilterType =
auto extract = ExtractFilterType::New();
// Note on direction cosines: Because our plane is in the xz-plane,
// the default submatrix would be invalid, so we must use the identity
extract->SetDirectionCollapseToIdentity();
extract->InPlaceOn();
extract->SetInput(composeRGB->GetOutput());
extract->SetExtractionRegion(coronalSlice);
using ImageWriterType = itk::ImageFileWriter<RGB2DImageType>;
auto writer = ImageWriterType::New();
writer->SetFileName(outputImageFile);
// this line is a request for the number of regions
// the image will be broken into
writer->SetNumberOfStreamDivisions(200);
writer->SetInput(extract->GetOutput());
itk::SimpleFilterWatcher watcher1(writer, "stream writing");
try
{
// update by streaming
writer->Update();
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// This example creates a RawImageIO and ImageSeriesReader for each
// color channel in the data. Notice that there are no special methods
// that are needed to enable streaming; it will just respond correctly
// to requests from the pipeline. In the ComposeImageFilter, the
// channels are composited into a single color image. Then the
// information is updated to initialize the coronal slice region to be
// extracted. The final filter, ImageFileWriter, writes out the file
// as a Meta Image type, which fully supports IO streaming.
//
// The most interesting aspect of this example is not the filters
// used, but how ITK’s pipeline manages its execution. The final
// output image is 2048 by 1878 pixels. The ImageFileWriter breaks
// this 2D image into 200 separate regions, which have the size of
// about 2048 by 10 pixels; each region is streamed and processes
// through the pipeline. The writer makes 200 calls to its ImageIO
// object to write the individual regions. The extractor converts this
// 2D region into a 3D region of 2048 by 1 by 10 pixels, which is
// propagated to the ImageSeriesReader. Then the reader reads the
// entire slice, but only copies the requested sub-region to its
// output. This pipeline is so efficient because very little data is
// actually processed at any one stage of the pipeline due to
// streaming IO.
return EXIT_SUCCESS;
}
itkExtractImageFilter.h
itk::RGBPixel
Represent Red, Green and Blue components for color images.
Definition: itkRGBPixel.h:58
itkComposeImageFilter.h
itk::ImageSeriesReader
Data source that reads image data from a series of disk files.
Definition: itkImageSeriesReader.h:45
itk::SimpleFilterWatcher
Simple mechanism for monitoring the pipeline events of a filter and reporting these events to std::co...
Definition: itkSimpleFilterWatcher.h:68
itkImageSeriesReader.h
itkNumericSeriesFileNames.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:90
itk::GTest::TypedefsAndConstructors::Dimension2::RegionType
ImageBaseType::RegionType RegionType
Definition: itkGTestTypedefsAndConstructors.h:54
itkRawImageIO.h
itkImageFileWriter.h
itkShrinkImageFilter.h
itk::NumericSeriesFileNames
Generate an ordered sequence of filenames.
Definition: itkNumericSeriesFileNames.h:54
itkSimpleFilterWatcher.h
itk::ImageRegion::SetIndex
void SetIndex(const IndexType &index)
Definition: itkImageRegion.h:181
itk::ComposeImageFilter
ComposeImageFilter combine several scalar images into a multicomponent image.
Definition: itkComposeImageFilter.h:62
itk::ExtractImageFilter
Decrease the image size by cropping the image to the selected region bounds.
Definition: itkExtractImageFilter.h:119
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
New
static Pointer New()
itk::RawImageIO
Read and write raw binary images.
Definition: itkRawImageIO.h:49
itkMeanImageFilter.h