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

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=== New LevelSets Segmentation Framework ===
=== 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 levels 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 ===
=== SimpleITK ===

Revision as of 21:59, 1 September 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 the External module.
  • Find the quality control per module by performing code coverage tests per module.

New Registration Framework

  • Automatic optimization parameter scale estimation
  • Composite transform
  • Displacement field transform
  • Better integration of dense displacement field registration into the framework
  • Better support for multi-threading
  • Additional evolutionary optimizers
  • Different B-Spline registration approach available
  • Transform symmetric second rank tensors

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 levels 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

WrapITK

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