ITK/Release 4/Why Switch to ITKv4: Difference between revisions

From KitwarePublic
< ITK‎ | Release 4
Jump to navigationJump to search
No edit summary
 
(One intermediate revision by one other user not shown)
Line 46: Line 46:
* Easy to use
* Easy to use
** Procedural Interface Examples:
** Procedural Interface Examples:
*** [[ http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussianProcedural_8py-example.html | Procedual Gaussian in Python ]]
*** [http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussianProcedural_8py-example.html Procedual Gaussian in Python ]
*** [[ http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussianFunctional_8cxx-example.html | Procedural Gaussian in C++]]
*** [http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussianFunctional_8cxx-example.html Procedural Gaussian in C++]
** Object Oriented Interface Examples:
** Object Oriented Interface Examples:
*** [[ http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8cxx-example.html | OOP Gaussian in C++ ]]
*** [http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8cxx-example.html OOP Gaussian in C++ ]
*** [[ http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8py-example.html | OOP Gaussian in Python ]]
*** [http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8py-example.html OOP Gaussian in Python ]
*** [[ http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8java-example.html | OOP Gaussian in Java ]]
*** [http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8java-example.html OOP Gaussian in Java ]
*** [[ http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8cs-example.html | OOP Gaussian in C# ]]
*** [http://www.itk.org/SimpleITKDoxygen/html/SimpleGaussian_8cs-example.html OOP Gaussian in C# ]
** Additional Examples:
** Additional Examples
*** http://www.itk.org/SimpleITKDoxygen/html/examples.html
*** http://www.itk.org/SimpleITKDoxygen/html/examples.html
** [[ITK_Release_4/Why_Switch_to_ITKv4/SimplifiedITK | Illustrations of ITK, WrappedITK, and SimpleITK]]
* Rapid development
* Rapid development
* Interactive processing
* Interactive processing

Latest revision as of 05:48, 30 December 2011

Modularization

  • Easier to find what you are looking for.
  • Easier to understand how to use the toolkit.
  • Build only the parts of the toolkit that you need.
  • Extend bridging of the toolkit to other toolkit and libraries.
  • Makes it easy to associate and build auxiliary community projects with an External module.
  • Find the quality control per module by performing code coverage tests per module.

New Finite Element Registration Framework

  • Better conformity with the rest of the toolkit
  • Improved IO of objects and results

New Registration Framework

  • New unified and fully multi-threaded optimization and registration framework
  • Unified framework supports sparse and dense metric computation
  • Unified framework supports low and high dimensional mapping
  • Improved multi-threaded metrics for rigid, affine and deformable registration
  • New metrics support sparse or dense sampling
  • Metrics for landmark or label guided registration
  • Automatic parameter scale estimation for registration
  • Automatic step-size selection for gradient-based registration optimizers
  • Composite transforms and composite transform optimization
  • Displacement field and diffeomorphic velocity field-based transforms
  • Better support for multi-threading in optimizers and metrics
  • Additional evolutionary optimizers
  • Improved B-Spline registration approach available and bug fixes to old framework
  • Accurately transform and reorient covariant tensors and vectors

New LevelSets Segmentation Framework

  • Powerful, modular architecture
    • Now you, too, can fully understand how level sets are implemented!
    • Easy to extend and customize
  • Simultaneously evolve multiple level sets on an image
  • Faster
    • Limit evolution to a domain
    • Three different sparse representations available, Whitaker, Shi, Malcolm
    • Design avoids duplication of calculations in many ways
  • Easy conversion from BinaryMask or LabelMap to a level set.

SimpleITK

Software Process

  • Fast, distributed, powerful version control: Git
  • Better support for making contributions
    • Low barrier to entry
    • Well defined contribution process
    • Use powerful tools, Git branches, Gerrit reviews
    • Get code into the toolkit quicker
    • Get higher quality code in with more reviews and platform testing
  • Forget SETI@home, give the aliens a reason to visit by improving ITK with CDash@home!
  • Know when/if your bug will be addressed with the scrum process in the JIRA issue tracker
  • Flexible management of testing data

WrapITK

  • Tight incorporation of the outstanding, formerly third party WrapITK project.
  • Numpy integration.

"You can do it!"

  • Migration Guide
  • ITKv4 Removed or renamed classes|
  • The insight-users mailing list is there for you.
  • Transition is easier with ITKV3_COMPATIBILITY configuration option.
  • Many deprecated classes are still available.
  • The list of other improvements and new features are legion, don't miss out!