[Insight-users] Re: reg similarity measure(deformable registration)

[Luis Ibanez]

Hi Dalal,

Please read the Tutorials in Image Registration available at

       http://www.itk.org/HTML/Tutorials.htm

in particular

http://www.itk.org/CourseWare/Training/RegistrationMethodsOverview.pdf
http://www.itk.org/CourseWare/Training/NonRigidRegistrationMethods.pdf

You should also read the chapter on Image Registration in
the ITK SoftwareGuide

          http://www.itk.org/ItkSoftwareGuide.pdf

--


About your questions:


1) Yes,

    You can use deformable registration without landmarks.
    As a matter of fact, none of the deformable registration
    methods that you listed require landmarks to be defined.

    You can optionally use landmarks in FEM if you feel
    inclined to do so.


2) "What method should give better results" ?

    That's a retoric question that only make sense in the
    "paper-publishing" context of "my method is better than
    yours".   That's only useful in the Matrix...

    In real life there is no such a thing as a "better method",
    since the performance of each method is extremly dependent
    on the parameters that you select for it. What you will find
    in real life is the ubiquitous trade-off of quality versus cost.

    Where cost involves computation time, memory allocation, user-time
    required for fine tunning parameters, and of course, amount of
    liters of coffee to be consumed in the process.


    So, turn the question around and ask yourself first:


     - How many weeks do I have to get this registration done ?

     - How long should a registration computation take ?

     - How many weekends and sleepless nights am I planning
       to pass fine tunning the parameters of the registration ?

     - How many journal and conference papers am I willing to
       study in order to get the background needed for getting
       the best out of this method ?

     - How much registration precision is required for the
       application for which this registration is intended ?


    Your choice of Deformable Registration method will be
    quite straight forward once you answer those questions   :-)


    Here are some guidelines
    that you may want to consider at that point:

    Demons will be appropriate if the deformations in the
    image are small. Where "small" is defined as "being in
    the range of the transition regions of the anatomical
    structures in the image". That is, if the transition in
    intensity between one tissue and another in your image
    is 3 pixels wide,... then  Demons will be limited to
    correct for deformation equal or smaller than 3 pixels.
    You can of course overcome this limitation by using
    Demons in a Multi-Resolution context.

    There are few parameters in Demons, but the
    computational load is high.



    BSplines have also few parameters to fit, mostly those of
    the optimizer.

    FEM is the most flexible method, probably the one from
    which you can get better quality results, but also the
    one that will require more effort on your part to get
    it working correctly in your images.


    I would suggest you to start with BSplines, using a small
    grid (e.g. 8x8x8), and if needed move to a larger grid.
    Using a larger grid will be needed only if your images
    have local deformations that go in different directions.




3) You are right in the first statement and wrong in
    the second.

    - Yes,
      Mutual-Information have prove to be appropriate for
      multi-modality registration.

    - Yes,
      ITK *does* provide implementations of the MutualInformation
      metric.

      There are actually four different implementation
      of Mutual Information in ITK

         - Viola Wells
http://www.itk.org/Insight/Doxygen/html/classitk_1_1MutualInformationImageToImageMetric.html

         - Mattes et al.
http://www.itk.org/Insight/Doxygen/html/classitk_1_1MattesMutualInformationImageToImageMetric.html

         - KullbackLeibler
http://www.itk.org/Insight/Doxygen/html/classitk_1_1KullbackLeiblerCompareHistogramImageToImageMetric.html#_details

         - Histogram based
http://www.itk.org/Insight/Doxygen/html/classitk_1_1MutualInformationHistogramImageToImageMetric.html



4) You will find the full list of image metrics available
    in ITK in the Doxygen documentation.

  http://www.itk.org/Insight/Doxygen/html/group__RegistrationMetrics.html

    As you can see there are twelve different Image-to-Image
    Metrics available in ITK at this point.

    You are certainly welcome to write new implementations of
    Image metrics. If you decide to do so, we strongly encourage
    you to contribute your code back to ITK so other users can
    take advantage of your efforts.




Regards,


    Luis



--------------------
Dalal, Dhaval wrote:
>  
> hey luis
> I know that ITK provides multiple methods for performing deformable
> registration. 
> 
> In particular: 
> 
> 1) BSpline Transform 
> 2) Demons deformable registration 
> 3) FEM-Based deformable registration 
> 
> I want to register 2 MRI datasets, their shapes are quite different 
> hence i will use deformabale registration method.
> my questions are
> 1) I do not have any fixed landmarks, is it possible to register without
>    known landmarks in these 3 methods. 
> 2) which of these 3 methods should give the better result.
> 3) Regarding the similarity metric, i think it is an accepted fact that 
>    mutual information gives the better result for multi-modal registration. 
> itk i do not think provides a framework for mutual information based
> registration? why is that so.
> 4) other similarity measures assume the intensity to be the same in both the
> images. not mutual information.
> what are the similarity measures implemented in ITK.
> is it possible to write the code for mutual information.
> Kindly help me out please.
> your help will be greatly appreciated.
> regards
> Dhaval Dalal 
>