[Insight-users] Narrow band to image registration

[Luis Ibanez]

Hi Eduard,


1) Won't be able to help you with the code
    that doesn't work... unless you tell us
    what happens when you run it:

    - does it crash  ?
    - does it aborts ?
    - does it runs indefinitely ?

    a bit more information may be useful  :-)


2)  The Kernel Splines simply use the point
     pairs in order to define Vectors, then
     interpolates those vectors in space.
     You can think of it just as the BSpline
     deformable transform: A mechanism for
     representing a deformation field.

     More details on how this transforms work
     are available at:
http://www.itk.org/Insight/Doxygen/html/classitk_1_1KernelTransform.html

     and of course in the original paper

      IEEE TMI,  Vol. 16, No. 3 June 1997
      Davis, Khotanzad, Flamig, and Harms,



3)  If you MRI image is hightly inhomogeneous, you
     should apply first the correction method in

         InsightApplications/MRIBiasCorrection

     You will find instructions in the README
     file in that directory.



4)  You can choose any image as the fixed
     or moving image. That shouldn't be a
     problem. The fundamental fact is that
     whatever you choose as the fixed image,
     that's the one you will have to segment
     in order to produce a PointSet representing
     the narrow band.


5)  For quantifying the deformation field and
     the quality of the registration, the easiest
     thing to do is to load all of them in ParaView.

     you can download sources and binaries of
     ParaView for free at:

           http://www.paraview.org

     by overlaping the deformation field to the
     fixed and moving images you can visually
     verify the correctness of the deformation.

     Once the results are visually satisfactory,
     you could move to other ways of quantifying
     registration quality.




Regards,



    Luis



--------------------------
Eduard Schreibmann wrote:

> First of all, thank you for your fast replay and hints. Did changes as you 
> said, seems to be ok as code, does not work in tests for some reason.
> 
> There are two theoretical things that are not clear
> 
> 
>>    I'll strongly suggest you to start with an AffineTransform
>>    and if that proves to be insufficient then try the KernelSplines.
> 
> 
> The kernel splines are not supposed to use 2 set of points, or I'm 
> understanding wrong ? If indeed 2 sets of points are needed, what would be 
> the second set ? 
>  
> 
>>b)
>>
>>There is more information about this method in a paper that
>>was submitted to a Journal last year, and you probably will
>>be able to read it next year...
>>
>>  Sorry the InsightJournal wasnt' there at the time  :-)
>>
> 
> 
> I suppose there isn't any possibility to read the manuscript. I "moved" 
> recently to the registration stuff, any theoretical info would help a lot.
> Or, can have the references list of the manuscript, or some references to 
> better understand the mathematics behind the code ?
> 
> 
>>----------------
>>
>>d)
>>
>>Yes the narrow band will work for Fuzzy image, because
>>*you* will segment them first   :-)
> 
> 
> The "problem" is that I prefer to have the mask/segmentation on the MRI 
> image and the fuzzy CT image as moving, otherwise we have to have 2 
> segmentations :) And the MRI image is also strange, lot of high intensity 
> pixels near the rectum falling of almost exponentially to some background 
> like intensities.
> 
> What is better to have as moving image, a fuzzy image or a 
> highly "inhomogeneous" (as pixel values) image.
> 
> How it is best to measure how good this algorithm is ? 
> I'm trying to warp the CT image and see how well the registration can 
> deformed it back. Is there a better way to "quantify" how good the 
> deformation is, except plotting the convergence or checkboard ?
> 
> 
>>Please let us know if you have further questions.
>>
> 
> 
> Thank you bery much for your replays, they really help.
> Edi
>  
>