Difference between revisions of "SimpleITK/Advisory Review Board/Prototype Code Discussions"

From KitwarePublic
Jump to navigationJump to search
Line 222: Line 222:


== Region Growing ==
== Region Growing ==
* Open an image
* Set up a set of seed points
* Run an connected threshold region growing segmentation
* Write out the resulting segmentation mask
==== Procedural ====
* Pros:
** Fewest lines of code
* Cons:
** Not modular
** More complex argument lists needed for different comparison types
<pre style="display: inline-block;">
// Open an image
Image::Pointer im = ImageFileReader::Execute("sample/path/to/image.jpg");
// Set up seeds
std::vector<Point> points;
Point p1, p2;
p1[0] = 25;
p1[1] = 25;
p2[0] = 35;
p2[1] = 5;
points.push_back(p1);
points.push_back(p2);
// Run the region growing algorithm
seg = RegionGrowingConnectedThresholdFilter::Execute(im, points, 128, 200);
// Write out the resulting segmentation
ImageFileWriter::Execute(seg, "sample/path/to/output.png");
</pre>


== Watershed ==
== Watershed ==


== QuadEdgeMesh ==
== QuadEdgeMesh ==

Revision as of 15:32, 7 September 2010

Gaussian Blur

  • Open an image
  • Filter the image with a Gaussian blur using sigma = 2
  • Write the image back out

Procedural

  • Pros:
    • Fewest lines of code to get a job done
    • Easy to learn/use ("Matlab-like")
  • Cons:
    • Biggest diversion from traditional ITK style
    • Potentially long list of parameters for a given function
// Read the image
Image::Pointer im = ImageFileReader::Execute("sample/path/to/image.jpg");

// Apply Gaussian with sigma = 2
im = GaussianFilter::Execute(im, 2);

// Write out the image
ImageFileWriter::Execute(im, "sample/path/to/output.png");

Filter Blocks

  • Pros:
    • Each filter processes an image directly
    • Default parameters make provide good guess for algorithm prototyping
    • Parameters can be changed individually as needed
  • Cons:
    • Image is fully processed between each filter (No easy streaming implementation)
  • Other Notes:
    • No pipeline
    • Uses object-oriented paradigm so filters must be instantiated


// Read the image
ImageFileReader reader;
reader.SetFilename( "sample/path/to/image.jpg" );
Image::Pointer im = reader.execute();

// Apply Gaussian with sigma = 2
Gaussian filter;
filter.SetSigma( 2 );
im = filter.execute( im );

// Write out the image
ImageFileWriter writer;
writer.SetFilename( "sample/path/to/output.png" );
writer.execute( im );

Pipelined

  • Pros:
    • Can easily implement streaming for large images
    • Default parameter list is good for prototyping
    • Can set individual parameters as needed
  • Cons:
    • Pipeline paradigm may be steeper on-ramp for new users
  • Other Notes:
    • Closest to full ITK implementation with templates removed
// Read the image
ImageFileReader reader;
reader.SetFilename( "sample/path/to/image.jpg" );

// Apply Gaussian with sigma = 2
Gaussian filter;
filter.SetSigma( 2 );
filter.SetInput( reader.getOutput() );

// Write out the image
ImageFileWriter writer;
writer.SetFilename( "sample/path/to/output.png" );
writer.SetInput( filter->GetOutput() );

// Execute the pipieline
writer.Update();

Image Registration

  • Open two images (one fixed, one moving)
  • Register the moving image to the fixed image using affine registration
  • Resample the moving image using the computed transform
  • Write the resampled image out

Procedural

  • Pros:
    • Fewest lines of code
  • Cons:
    • Long argument lists for functions
    • Not easily extensible w.r.t. transform type, optimization type, interpolation type, etc...
  • Other Notees:
    • Most "Matlab-like"
// Open the fixed and moving images
Image::Pointer fixedImage = ImageFileReader::Execute( "path/to/fixed.jpg" );
Image::Pointer movingImage = ImageFileReader::Execute( "path/to/moving.jpg" );

// Register the moving image to the fixed image
AffineTransform::Pointer transform AffineRegistrator::Execute( fixedImage, movingImage );

// Resample the moving image
movingImage = ImageResampler::Execute( movingImage, transform );

// Write out the resampled image
ImageFileWriter::Ececute( movingImage, "path/to/output.png" );

Filter blocks

  • Pros:
    • Default parameters make provide good guess for algorithm prototyping
    • Parameters can be changed individually as needed
  • Cons:
    • Image is fully processed when execute is called (No easy streaming implementation)
  • Other Notes:
    • No pipeline
    • Uses object-oriented paradigm
// Open the fixed and moving images
ImageFileReader reader;
reader.SetFilename( "path/to/fixed.jpg" );
Image::Pointer fixedImage = reader.execute();
reader.SetFilename( "path/to/moving.jpg" );
Image::Pointer movingImage = reader.execute();

// Register the moving image to the fixed image
AffineRegistrator registrator;
registrator.SetFixedImage( fixedImage );
registrator.SetMovingImage( movingImage );
AffineTransform transform;
transform = registrator.execute();

// Resample the moving image
Resampler resampler;
resampler.SetTransform( transform );
movingImage = resampler.execute( movingImage );

// Write out the resampled image
ImageFileWriter writer;
writer.SetFilename( "path/to/output.png" );
writer.ececute( movingImage );

Pipeline

// Open the fixed and moving images
ImageFileReader reader1;
ImageFileReader reader2;
reader1.SetFilename( "path/to/fixed.jpg" );
reader2.SetFilename( "path/to/moving.jpg" );

// Register the moving image to the fixed image
AffineRegistrator registrator;
registrator.SetFixedImage( reader1.GetOutput() );
registrator.SetMovingImage( reader2.GetOutput() );

// Resample the moving image
Resampler resampler;
resampler.SetInput( reader2.GetOutput() );
resampler.SetTransform( registrator.GetOutput() );

// Write out the resampled image
ImageFileWriter writer;
writer.SetFilename( "path/to/output.png" );
writer.SetInput( movingImage );

// Execute the pipeline
writer.Update();

Enum approach

  • Notes:
    • 2 independent issues here (enums and granularity)
    • Uses enums for parameter setting so all parameter setting is done through an X.SetParameter[TYPE]("name", val) function
    • Maintains registration granularity so that transform, interpolator, optimizer, and metric can be interchanged
// NOTE: Language = C#
// Create metric
itk::simple::simpleMetric metric;
metric.setType( itk::simple::MattesMutualInformation );
metric.setParameterInt( "NumberOfHistogramBins", 30 );
metric.setParameterInt( "NumberOfSpatialSamples", 1000 );

// Create interpolator
itk::simple::simpleInterpolator interpolator;
interpolator.setType( itk::simple::LanczosWindowedSincInterpolation );

// Create transform
itk::simple::simpleTransform transform;
transform.setType( itk::simple::AffineTransform );

// Create optimizer
itk::simple::simpleOptimizer optimizer;
optimizer.setType( itk::simple::RegularStepGradientDescentOptimizer );
optimizer.setParameterInt( "NumberOfIterations", 100 );
optimizer.setParameterDouble( "MinimumStepLength", 0.005 );
optimizer.setParameterDouble( "MaximumStepLength", 1.0 );
optimizer.setParameterBoolean( "Maximize", true );

// Registration
itk::simple::simpleRegistration registration;
registration.setMetric( metric );
registration.setInterpolator( interpolator );
registration.setTransform( transform );
registration.setOptimizer( optimizer );
registration.setFixedImage( fixedImage );
registration.setMovingImage( movingImage );

Level Set

Region Growing

  • Open an image
  • Set up a set of seed points
  • Run an connected threshold region growing segmentation
  • Write out the resulting segmentation mask

Procedural

  • Pros:
    • Fewest lines of code
  • Cons:
    • Not modular
    • More complex argument lists needed for different comparison types
// Open an image
Image::Pointer im = ImageFileReader::Execute("sample/path/to/image.jpg");

// Set up seeds
std::vector<Point> points;
Point p1, p2;
p1[0] = 25;
p1[1] = 25;
p2[0] = 35;
p2[1] = 5;
points.push_back(p1);
points.push_back(p2);

// Run the region growing algorithm
seg = RegionGrowingConnectedThresholdFilter::Execute(im, points, 128, 200);

// Write out the resulting segmentation
ImageFileWriter::Execute(seg, "sample/path/to/output.png");

Watershed

QuadEdgeMesh