ITK Registration Optimization
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Revision as of 00:18, 2 April 2007 by Aylward (talk | contribs) (→MattesMutualInformationImageToImageMetric)
Home < ITK Registration Optimization
Goals
There are two components to this research
- Identify registration algorithms that are suitable for non-rigid registration problems that are indemic to NA-MIC
- Develop implementations of those algorithms that take advantage of multi-core and multi-processor hardware.
Algorithmic Requirements and Use Cases
- Requirements
- relatively robust, with few parameters to tweak
- runs on grey scale images
- has already been published
- relatively fast (ideally speaking a few minutes for volume to volume).
- not patented
- can be implemented in ITK and parallelized.
- Use-cases
- Intersubject mapping
- Example data set (Kilian)
- fMRI to hi-res brain morphology mapping
- Example data set (Steve Pieper)
- DTI: components of the diffusion tensor
- Example data (Sylvain)
- Intersubject mapping
Hardware Platform Requirements and Use Cases
- Requirements
- Shared memory
- Single and multi-core machines
- Single and multi-processor machines
- AMD and Intel - Windows, Linux, and SunOS
- Use-cases
- Intel Core2Duo
- Intel quad-core Xeon processors (?)
- 6 CPU Sun, Solaris 8 (SPL: vision)
- 12 CPU Sun, Solaris 8 (SPL: forest and ocean)
- 16 core Opteron (SPL: john, ringo, paul, george)
- 16 core, Sun Fire, AMDOpteron (UNC: Styner)
Data
Workplan
Establish testing and reporting infrastructure
- Identify timing tools
- Cross platform and multi-threaded
- Timing and profiling
- Status
- Instrumenting modular tests
- Extending itk's cross-platform high precision timer
- Adding thread affinity to ensure valid timings
- Adding method for increasing process priority
- Profiling complete registration solutions for use cases
- Using CacheGrind on single and multi-core linux systems
- Instrumenting modular tests
- Develop performance dashboard for collecting results
- Each test will report time and accuracy to a central server
- The performance of a test, over time, for a given platform can be viewed on one page
- The performance of a set of tests, at one point in time, for all platforms can be viewed on one page
- Status
- BatchMake database communication code being isolated
- Performance dashboard web pages being designed
Develop tests
- Develop modular tests
- Status
- Developed itkCheckerboardImageSource so no IO required
- Developing tests as listed in the "Modular Tests" section below
- Status
- Develop C-style tests
- Tests should represent the non-ITK way of doing image analysis
- Use standard C/C++ arrays and pointers to access blocks of memory as images
- Tests should represent the non-ITK way of doing image analysis
- Develop complete registration solutions for use cases
- Status
- Centralized data and provide easy access
- Identified relevant registration algorithms
- rigid, affine, bspline, multi-level bspline, and Demons'
- normalized mutual information, mean squared difference, and cross correlation
- Developing traditional ITK-style implementations
- Status
Compute performance on target platforms
- Ongoing
Optimize bottlenecks
- Target bottlenecks
- Use random, sub-sampling iterator in mean squared difference and cross correlation
- Multi-thread metric calculation
- Integrate metrics with transforms and interpolators for tailored performance
MattesMutualInformationImageToImageMetric
Time in Functions
Time in self | Time in subfuncs | Function |
---|---|---|
0.00 | 86.64 | __tmainCRTStartup" |
0.00 | 48.47 | main" |
0.00 | 37.98 | itk::MattesMutualInformationImageToImageMetric<itk::Image<float,3>,itk::Image<float,3> >::GetDerivative" |
8.49 | 20.99 | itk::MattesMutualInformationImageToImageMetric<itk::Image<float,3>,itk::Image<float,3> >::GetValueAndDerivative" |
0.57 | 19.27 | itk::CentralDifferenceImageFunction<itk::Image<float,3>,double>::Evaluate" |
13.40 | 13.55 | itk::CentralDifferenceImageFunction<itk::Image<float,3>,double>::EvaluateAtIndex" |
11.70 | 11.83 | itk::BSplineKernelFunction<3>::Evaluate [1]" |
9.06 | 9.16 | itk::BSplineKernelFunction<2>::Evaluate [1]" |
3.21 | 8.40 | itk::MattesMutualInformationImageToImageMetric<itk::Image<float,3>,itk::Image<float,3> >::GetValue" |
8.11 | 8.21 | floor ?" |
0.00 | 7.25 | itk::CheckerBoardImageSource<itk::Image<float,3> >::GenerateData" |
0.00 | 4.20 | itk::ImageSource<itk::Image<float,3> >::ThreaderCallback" |
3.77 | 3.82 | itk::NearestNeighborInterpolateImageFunction<itk::Image<float,3>,double>::EvaluateAtContinuousIndex" |
3.21 | 3.24 | itk::StatisticsImageFilter<itk::Image<float,3> >::ThreadedGenerateData" |
3.02 | 3.05 | itk::ImageFunction<itk::Image<float,3>,double,double>::IsInsideBuffer" |
2.83 | 2.86 | itk::BSplineDerivativeKernelFunction<3>::Evaluate" |
2.26 | 2.29 | itk::InterpolateImageFunction<itk::Image<float,3>,double>::Evaluate" |
0.00 | 2.10 | endthreadex ?" |
2.08 | 2.10 | itk::MattesMutualInformationImageToImageMetric<itk::Image<float,3>,itk::Image<float,3> >::TransformPoint" |
2.08 | 2.10 | thunk@403355 ?" |
1.89 | 1.91 | _ftol2_pentium4" |
1.70 | 1.72 | itk::MattesMutualInformationImageToImageMetric<itk::Image<float,3>,itk::Image<float,3> >::ComputePDFDerivatives" |
1.70 | 1.72 | thunk@402f54 ?" |
1.51 | 1.53 | itk::BSplineKernelFunction<2>::Evaluate" |
1.51 | 1.53 | itk::ImageBase<3>::GetSpacing" |
1.13 | 1.53 | itk::ImageFunction<itk::Image<float,3>,double,double>::ConvertContinuousIndexToNearestIndex" |
0.94 | 1.34 | itk::CheckerBoardSpatialFunction<double,3,itk::Point<double,3> >::Evaluate" |
1.13 | 1.15 | itk::ImageFunction<itk::Image<float,3>,double,double>::IsInsideBuffer [1]" |
0.94 | 0.95 | itk::BSplineKernelFunction<3>::Evaluate" |
0.75 | 0.76 | itk::ImageBase<3>::GetBufferedRegion" |
0.75 | 0.76 | itk::Point<double,3>::operator+" |
0.75 | 0.76 | thunk@4036d4 ?" |
0.75 | 0.76 | thunk@403cec ?" |
0.19 | 0.57 | itk::ImageFunction<itk::Image<float,3>,itk::CovariantVector<double,3>,double>::ConvertContinuousIndexToNearestIndex" |
0.57 | 0.57 | itk::MattesMutualInformationImageToImageMetric<itk::Image<float,3>,itk::Image<float,3> >::ComputeImageDerivatives" |
0.57 | 0.57 | itk::Point<double,3>::operator=" |
0.57 | 0.57 | itk::ShiftScaleImageFilter<itk::Image<float,3>,itk::Image<float,3> >::ThreadedGenerateData" |
0.57 | 0.57 | itk::TranslationTransform<double,3>::TransformPoint" |
Time in files
Time in self | Time in subfuncs | Files |
---|---|---|
16.42 | 72.33 | itkmattesmutualinformationimagetoimagemetric.txx" |
0.00 | 48.47 | mattesmutualinformationimagetoimagemetrictest.cxx" |
23.21 | 23.47 | itkbsplinekernelfunction.h" |
0.57 | 19.27 | itkcentraldifferenceimagefunction.h" |
13.40 | 13.55 | itkcentraldifferenceimagefunction.txx" |
0.00 | 7.25 | itkcheckerboardimagesource.txx" |
5.66 | 6.49 | itkimagefunction.h" |
0.00 | 4.20 | itkimagesource.txx" |
3.77 | 3.82 | itknearestneighborinterpolateimagefunction.h" |
3.21 | 3.24 | itkstatisticsimagefilter.txx" |
2.83 | 2.86 | itkbsplinederivativekernelfunction.h" |
2.83 | 2.86 | itkimagebase.h" |
2.26 | 2.29 | itkinterpolateimagefunction.h" |
0.94 | 1.34 | itkcheckerboardspatialfunction.txx" |
1.32 | 1.34 | itkpoint.txx" |
0.75 | 0.76 | itktranslationtransform.txx" |
0.57 | 0.57 | itkshiftscaleimagefilter.txx" |
0.38 | 0.38 | vnl_matrix.txx" |
0.00 | 0.19 | itkbsplinedeformabletransform.txx" |
0.19 | 0.19 | itkfixedarray.txx" |
0.19 | 0.19 | itkimageregionconstiterator.txx" |
0.19 | 0.19 | itkobject.cxx" |
0.19 | 0.19 | vnl_vector.txx" |
0.19 | 0.19 | vector" |
0.19 | 0.19 | secchk.c" |
)
Modular testsAll tests send two values to performance dashboards
Tests being developed and their parameter spaces
Notes
Related PagesPerformance Measurement
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